基于随机点积图理论的模式识别方法研究
|
摘要
随着计算机技术与人工智能理论的发展,模式识别的理论与方法研究已经取得很大进展,并已广泛应用于声音和语言识别、文字识别、指纹识别、图像分析等领域。近年来,网络数据的分析和处理成为模式识别的重要研究内容。面对网络这种新型、动态的大规模关系数据,随机图及其所衍生出的复杂网络理论受到越来越多的关注。
研究表明,随机图可以更好地模拟现实的关系数据,在分类、聚类、匹配等模式识别经典问题中都显示出明显优势与发展潜力。本文立足于一种重要的随机图模型——随机点积图,重点研究了随机点积图在自动图像标注、多社团属性关系传播、网络攻击检测等多个模式识别新兴热点问题中的应用,并从理论上对随机点积图在保持模长归一化的约束下进行了进一步的推广。
随机点积图是近年来新提出的一种点-边随机图模型,它通过对节点的随机赋值,依照点积规则计算节点之间的连接概率,从而通过节点的随机性体现出边的随机性,形成随机图。随机点积图具有聚类性、传递性、度幂律性等多种重要性质,可以很好地拟合现实存在的各种图结构和网络。本文从概率期望的角度证明了随机点积图的传递性,将在一维空间中的证明过程推广到高维空间中；传统的传递性质只涉及节点连通时的情况,本文提出了在随机点积图中节点不连通时边概率的传递性,并给予证明。对于随机点积图的求解问题,本文研究了随机点积图对关联图的模拟,并给出求解方法。该解法从关联图的加权邻接矩阵出发,将关联图的随机点积化问题转化成了矩阵范数逼近问题,通过对加权邻接矩阵的谱分解得到节点的赋值。
图像标注是基于内容的图像检索的重要和具有挑战性的课题。随着数字图像数据量呈爆炸性增长,如何有效检索海量的图像数据是个人与商业搜索引擎都迫切需要考虑的问题。自动图像标注能提供更符合人类检索习惯的文本输入查询方式,是图像检索中的一项关键技术。本文提出了一种基于随机点积图的图像标注算法,该算法首先构造了一个融合了底层特征间、标注词间以及图像与标注词间的相似关系的关联图,再利用随机点积图对该关联图进行重构,挖据出图像的底层特征间和标注词间隐藏的相似关系,并形成状态转移概率,结合重启式随机游走,最终实现自动图像标注。基于随机点积图的图像标注算法将基本标注阶段与标注改善阶段结合起来,从整体进行关联图的随机点积重构,并实现自动标注。在多个通用图像库上的实验证明,该方法可以有效提高图像标注精度,尤其在图像库较小时,具有明显优势。
近年来社会网络的研究取得了高速发展,其应用也越来越普及。与传统的模式识别不同,网络分析侧重个体之间相互联系的分析和挖掘,所以从模式识别的角度来看,网络分析也称为“链接识别”(Link recognition)或者“链接分析”(Link analysis)。在网络中,个体与个体之间围绕共同的兴趣和话题相互联系形成不同的社团。当前,社团已经成为了解网络结构、功能和增长机制的重要工具。由于不同社团中存在的数据关系大不相同,社团之间属性关系的传播已成模式识别中一个挑战性的问题。本文提出了一种基于随机点积图的多社团属性关系传播算法。该方法从已知属性关系的社团入手,结合目标社团中的个体特征,用随机点积图对当前属性关系不断演化,挖掘出目标社团中隐藏的属性关系。该方法可以同时实现对社团中成员的划分与属性关系的跨社团传递。通过在多个实际社会网络数据库的实验表明,该方法可以准确揭示社团中隐藏的属性关系。
数据降维与嵌入是模式识别中的重要研究问题。对于关系数据,随机点积图可以将图中的节点嵌入到向量空间中。关系数据经过核函数形成的相似矩阵往往具有相同的对角元,基于这一重要性质,本文提出一种改进的随机点积图模型——保持模长归一化的随机点积图,它可以将图嵌入到一个球面空间中。此外,对于归一化的特征数据,现有的降维方法都没有考虑数据的归一化性质,将保持模长归一化的随机点积图模型用于这类数据的降维中,则降维后的特征数据依然是模长归一化的。在这种随机点积图模型的解空间中,欧氏距离与夹角余弦是等价的。本文从理论上给出了该模型的求解方法与收敛性分析。在多个真实数据库上的聚类实验表明,该模型可以得到更具可分性的节点嵌入结果。
随着互联网技术的发展,大规模的动态网络通过计算机和其他设备将人类连接起来,这种大规模网络已经成为人们获取信息和知识的重要来源。为增强网络用户的安全性,网络攻击行为检测成为模式识别在网络分析中亟待解决的新问题。本文提出了一种新的基于保持模长归一化随机点积图的网络攻击检测方法,根据待测网络拓扑结构的随机点积图谱空间坐标识别欺骗或攻击。本文从理论上证明了攻击者与普通节点分别落在谱空间的不同区域中。保持模长归一化随机点积图将节点的谱坐标合理分布于球面空间中,并在该球面空间中识别攻击行为,尤其可以探测出在原始网络拓扑结构中难以识别的协同攻击。与现有基于拓扑的攻击检测方法相比较,对于各种形式的协同攻击,本文方法可以显著提高攻击检测的有效性及效率。
With the rapid development of computer technology and artificial intelligence theory, pattern recognition accordingly has been widely used in many fields, such as sound and language recognition, character recognition, fingerprint identification, image analysis and so on. In recent years, web data analyzing and processing becomes an important research topic of pattern recognition. Web data are dynamical and large scale relational data. To deal with web data, the random graph and it's derivatives:complex network have been paid more and more attentions by researchers.
In many classic problems of pattern recognition, such as classification, clustering and matching, random graph has apparent advantages and development potential. This dissertation focuses on a new important kind of random graph:random dot product graph, and mainly researches the application of random dot product graph in automatic image annotation, relational communication between communities and web attack detection. Moreover, the random dot product graph is extended theoretically with normalized constraint and some useful results are obtained.
Random dot product graph is a kind of vertex-edge random graph, in which a vector of attributes is assigned to each vertex, such that the probability of an edge is equal to the dot product of the vectors. Random dot product graph have some excellent properties, such as clustering, transitivity, power-law degree, and can fit to real-world graph very well. In this dissertation, we prove the transitivity of random dot product graph from a probabilities perspective; extend the property from one dimensional space to higher dimensional space. Only the connectivity situation is involved in the traditional transitivity, here we also proposed the transitivity when the nodes are not connected in the random dot product graph and prove it theoretically. Moreover, the relational graph fitting with random dot product graph also is studied. We convert this fitting to a Frobenius norm approximation problem, and acquire the node vectors by the eigen-decomposition of the weighted adjacency matrix.
Automatic image annotation is an important and challenging work in content-based image retrieval. With the number of digital images increases explosively, image retrieval in large-scale database has became an urgent problem to be solved for individual and commercial search engine. Automatic image annotation can provide query methods based on text input, which conforms to the human retrieval habit more sensibly. In order to overcome the semantic gap between low-level features and high-level semantic concept of image, a novel image annotation approach based on Random Dot Product Graph is proposed in this dissertation. In our approach, the relation between images and words are used to construct relational graph of image annotations. Then, we reconstruct the graph with Random Dot Product Graph, find the unobserved relevance in incompletely observed graph and transform the random graph into the probabilities of state transition. Combined with Random Walk with Restart (RWR), the final annotations are chosen. This new method incorporates the two sub-processes, i.e., basic image annotation and annotation refinement, and reduces the influence of the scale of database. Experiments conducted on three standard databases demonstrate that our approach outperforms the existing image annotation techniques.
In recent years, due to high-speed development and widespread application, web data analysis has become a focal problem. Different from the traditional pattern recognition, web data analysis focus on mining the relation among individual. So from the point of view of pattern recognition, web data analysis is also called link recognition or link analysis. One web feature that has been emphasized in recent work is community structure, the gathering of vertices into groups such that there is a higher density of interrelationship within groups than between them. The attributed relation propagation of multi-communities has become an extremely important tool for understanding the structure and the functioning of the network, and its growth mechanisms. This dissertation proposes a novel and efficient approach, based on random dot product graph, to exploit attributed relation while considering the community transition for large scale social network. Starting with the known attributed relation and initial classification results, the proposed approach evolve the hidden attributed relation in target community with random dot product graph. Particularly, the proposed approach is capable of simultaneously improving the classification results and propagating the concept affinities to target community, which preserves the consistency and smoothness of the attributed relation over multi-communities. We conduct extensive experiments to improve annotation results of videos from TRECVID2005-2007data sets. Results show consistent and significant performance gain over various methods.
Dimensinality reduction and embedding is important topics in pattern recognition as well. For relational data, random dot product graph can be used to embed the graph nodes into vector space. However, for most of the relation data, the kernelized similarity matrices have constant diagonal elements. Based on this characteristic, in this dissertation, we propose to employ a normalization preserved random dot product graph to embed graph data, which corresponds to embedding them on a unit spherical surface. Moreover, for normalized vector data, existing methods do not utilize the fact that the data is normalized. We also use the improved random dot product graph to deal with this normalized vector data, which preserves the normalization of original vector data. In the normalization preserved random dot product graph, the Euclidean distance is equivalent to the cosine similarity. Thus data structures best described in the cosine similarity and data structure best captured by the Euclidean distance can both be effectively detected in our approach. We provide the theoretical analysis, derive the computational algorithm, and evaluate the angular embedding on several datasets. Experiments on data clustering demonstrate that our method can provide a more discriminative subspace.
In recent decades, we have witnessed a global dynamical network that links humans through the use of computers and other devices. People communicate and interact spontaneously in a cyber space, create content and share knowledge over this large-scale networks. In large-scale and dynamic networks, each participant is vulnerable to various attacks. Thus, attack detection techniques need to be developed for enhancing security. In this dissertation, we propose a novel framework that detects web attack based on the normalization preserved random dot product graph. This method exploits the spherical spectral space of underlying network topology to identify frauds or attacks. Our theoretical results showed that attackers locate in a different region of the spectral space from regular users. By identifying attacking patterns in graph spherical spectral spaces, we can detect various collaborative attacks that are hard to be identified from the original topological structures. We compare our detection approach with the traditional topology based detection approach. Evaluation results show that our approach significantly improves both effectiveness and efficiency in detecting various collaborative attacks.
研究表明,随机图可以更好地模拟现实的关系数据,在分类、聚类、匹配等模式识别经典问题中都显示出明显优势与发展潜力。本文立足于一种重要的随机图模型——随机点积图,重点研究了随机点积图在自动图像标注、多社团属性关系传播、网络攻击检测等多个模式识别新兴热点问题中的应用,并从理论上对随机点积图在保持模长归一化的约束下进行了进一步的推广。
随机点积图是近年来新提出的一种点-边随机图模型,它通过对节点的随机赋值,依照点积规则计算节点之间的连接概率,从而通过节点的随机性体现出边的随机性,形成随机图。随机点积图具有聚类性、传递性、度幂律性等多种重要性质,可以很好地拟合现实存在的各种图结构和网络。本文从概率期望的角度证明了随机点积图的传递性,将在一维空间中的证明过程推广到高维空间中；传统的传递性质只涉及节点连通时的情况,本文提出了在随机点积图中节点不连通时边概率的传递性,并给予证明。对于随机点积图的求解问题,本文研究了随机点积图对关联图的模拟,并给出求解方法。该解法从关联图的加权邻接矩阵出发,将关联图的随机点积化问题转化成了矩阵范数逼近问题,通过对加权邻接矩阵的谱分解得到节点的赋值。
图像标注是基于内容的图像检索的重要和具有挑战性的课题。随着数字图像数据量呈爆炸性增长,如何有效检索海量的图像数据是个人与商业搜索引擎都迫切需要考虑的问题。自动图像标注能提供更符合人类检索习惯的文本输入查询方式,是图像检索中的一项关键技术。本文提出了一种基于随机点积图的图像标注算法,该算法首先构造了一个融合了底层特征间、标注词间以及图像与标注词间的相似关系的关联图,再利用随机点积图对该关联图进行重构,挖据出图像的底层特征间和标注词间隐藏的相似关系,并形成状态转移概率,结合重启式随机游走,最终实现自动图像标注。基于随机点积图的图像标注算法将基本标注阶段与标注改善阶段结合起来,从整体进行关联图的随机点积重构,并实现自动标注。在多个通用图像库上的实验证明,该方法可以有效提高图像标注精度,尤其在图像库较小时,具有明显优势。
近年来社会网络的研究取得了高速发展,其应用也越来越普及。与传统的模式识别不同,网络分析侧重个体之间相互联系的分析和挖掘,所以从模式识别的角度来看,网络分析也称为“链接识别”(Link recognition)或者“链接分析”(Link analysis)。在网络中,个体与个体之间围绕共同的兴趣和话题相互联系形成不同的社团。当前,社团已经成为了解网络结构、功能和增长机制的重要工具。由于不同社团中存在的数据关系大不相同,社团之间属性关系的传播已成模式识别中一个挑战性的问题。本文提出了一种基于随机点积图的多社团属性关系传播算法。该方法从已知属性关系的社团入手,结合目标社团中的个体特征,用随机点积图对当前属性关系不断演化,挖掘出目标社团中隐藏的属性关系。该方法可以同时实现对社团中成员的划分与属性关系的跨社团传递。通过在多个实际社会网络数据库的实验表明,该方法可以准确揭示社团中隐藏的属性关系。
数据降维与嵌入是模式识别中的重要研究问题。对于关系数据,随机点积图可以将图中的节点嵌入到向量空间中。关系数据经过核函数形成的相似矩阵往往具有相同的对角元,基于这一重要性质,本文提出一种改进的随机点积图模型——保持模长归一化的随机点积图,它可以将图嵌入到一个球面空间中。此外,对于归一化的特征数据,现有的降维方法都没有考虑数据的归一化性质,将保持模长归一化的随机点积图模型用于这类数据的降维中,则降维后的特征数据依然是模长归一化的。在这种随机点积图模型的解空间中,欧氏距离与夹角余弦是等价的。本文从理论上给出了该模型的求解方法与收敛性分析。在多个真实数据库上的聚类实验表明,该模型可以得到更具可分性的节点嵌入结果。
随着互联网技术的发展,大规模的动态网络通过计算机和其他设备将人类连接起来,这种大规模网络已经成为人们获取信息和知识的重要来源。为增强网络用户的安全性,网络攻击行为检测成为模式识别在网络分析中亟待解决的新问题。本文提出了一种新的基于保持模长归一化随机点积图的网络攻击检测方法,根据待测网络拓扑结构的随机点积图谱空间坐标识别欺骗或攻击。本文从理论上证明了攻击者与普通节点分别落在谱空间的不同区域中。保持模长归一化随机点积图将节点的谱坐标合理分布于球面空间中,并在该球面空间中识别攻击行为,尤其可以探测出在原始网络拓扑结构中难以识别的协同攻击。与现有基于拓扑的攻击检测方法相比较,对于各种形式的协同攻击,本文方法可以显著提高攻击检测的有效性及效率。
With the rapid development of computer technology and artificial intelligence theory, pattern recognition accordingly has been widely used in many fields, such as sound and language recognition, character recognition, fingerprint identification, image analysis and so on. In recent years, web data analyzing and processing becomes an important research topic of pattern recognition. Web data are dynamical and large scale relational data. To deal with web data, the random graph and it's derivatives:complex network have been paid more and more attentions by researchers.
In many classic problems of pattern recognition, such as classification, clustering and matching, random graph has apparent advantages and development potential. This dissertation focuses on a new important kind of random graph:random dot product graph, and mainly researches the application of random dot product graph in automatic image annotation, relational communication between communities and web attack detection. Moreover, the random dot product graph is extended theoretically with normalized constraint and some useful results are obtained.
Random dot product graph is a kind of vertex-edge random graph, in which a vector of attributes is assigned to each vertex, such that the probability of an edge is equal to the dot product of the vectors. Random dot product graph have some excellent properties, such as clustering, transitivity, power-law degree, and can fit to real-world graph very well. In this dissertation, we prove the transitivity of random dot product graph from a probabilities perspective; extend the property from one dimensional space to higher dimensional space. Only the connectivity situation is involved in the traditional transitivity, here we also proposed the transitivity when the nodes are not connected in the random dot product graph and prove it theoretically. Moreover, the relational graph fitting with random dot product graph also is studied. We convert this fitting to a Frobenius norm approximation problem, and acquire the node vectors by the eigen-decomposition of the weighted adjacency matrix.
Automatic image annotation is an important and challenging work in content-based image retrieval. With the number of digital images increases explosively, image retrieval in large-scale database has became an urgent problem to be solved for individual and commercial search engine. Automatic image annotation can provide query methods based on text input, which conforms to the human retrieval habit more sensibly. In order to overcome the semantic gap between low-level features and high-level semantic concept of image, a novel image annotation approach based on Random Dot Product Graph is proposed in this dissertation. In our approach, the relation between images and words are used to construct relational graph of image annotations. Then, we reconstruct the graph with Random Dot Product Graph, find the unobserved relevance in incompletely observed graph and transform the random graph into the probabilities of state transition. Combined with Random Walk with Restart (RWR), the final annotations are chosen. This new method incorporates the two sub-processes, i.e., basic image annotation and annotation refinement, and reduces the influence of the scale of database. Experiments conducted on three standard databases demonstrate that our approach outperforms the existing image annotation techniques.
In recent years, due to high-speed development and widespread application, web data analysis has become a focal problem. Different from the traditional pattern recognition, web data analysis focus on mining the relation among individual. So from the point of view of pattern recognition, web data analysis is also called link recognition or link analysis. One web feature that has been emphasized in recent work is community structure, the gathering of vertices into groups such that there is a higher density of interrelationship within groups than between them. The attributed relation propagation of multi-communities has become an extremely important tool for understanding the structure and the functioning of the network, and its growth mechanisms. This dissertation proposes a novel and efficient approach, based on random dot product graph, to exploit attributed relation while considering the community transition for large scale social network. Starting with the known attributed relation and initial classification results, the proposed approach evolve the hidden attributed relation in target community with random dot product graph. Particularly, the proposed approach is capable of simultaneously improving the classification results and propagating the concept affinities to target community, which preserves the consistency and smoothness of the attributed relation over multi-communities. We conduct extensive experiments to improve annotation results of videos from TRECVID2005-2007data sets. Results show consistent and significant performance gain over various methods.
Dimensinality reduction and embedding is important topics in pattern recognition as well. For relational data, random dot product graph can be used to embed the graph nodes into vector space. However, for most of the relation data, the kernelized similarity matrices have constant diagonal elements. Based on this characteristic, in this dissertation, we propose to employ a normalization preserved random dot product graph to embed graph data, which corresponds to embedding them on a unit spherical surface. Moreover, for normalized vector data, existing methods do not utilize the fact that the data is normalized. We also use the improved random dot product graph to deal with this normalized vector data, which preserves the normalization of original vector data. In the normalization preserved random dot product graph, the Euclidean distance is equivalent to the cosine similarity. Thus data structures best described in the cosine similarity and data structure best captured by the Euclidean distance can both be effectively detected in our approach. We provide the theoretical analysis, derive the computational algorithm, and evaluate the angular embedding on several datasets. Experiments on data clustering demonstrate that our method can provide a more discriminative subspace.
In recent decades, we have witnessed a global dynamical network that links humans through the use of computers and other devices. People communicate and interact spontaneously in a cyber space, create content and share knowledge over this large-scale networks. In large-scale and dynamic networks, each participant is vulnerable to various attacks. Thus, attack detection techniques need to be developed for enhancing security. In this dissertation, we propose a novel framework that detects web attack based on the normalization preserved random dot product graph. This method exploits the spherical spectral space of underlying network topology to identify frauds or attacks. Our theoretical results showed that attackers locate in a different region of the spectral space from regular users. By identifying attacking patterns in graph spherical spectral spaces, we can detect various collaborative attacks that are hard to be identified from the original topological structures. We compare our detection approach with the traditional topology based detection approach. Evaluation results show that our approach significantly improves both effectiveness and efficiency in detecting various collaborative attacks.
引文
[1]Jain, A.K., Duin, R.P.W., Mao, J.,2000. Statistical pattern recognition:a review, IEEE Transaction of Pattern Analysis and Machine Intelligence.22,4-37.
[2]F. Rosenblatt, The perceptron:a probabilistic model for information storage and organization in the brain, Psychological Review,65:386-408,1958.
[3]N.J. Nilsson, Learning Machines[M], McGraw-Hill, New York,1965.
[4]R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification (2nd edition) [M], Wiley Interscience, New York,2001.
[5]D.L. Donoho, High-Dimensional Data Analysis:The Curses and Blessings of Dimensionality[C], In Proceeding of International Conference of Mathematicians (AMS). Challenges of the 21st Century,2000.
[6]J.Scott. Social network analysis, Sociology the journal of the British sociological association,1(22):109-127,1994.
[7]M.Klusch, B. Fries, K, Sycara. Automated semantic web service discovery with OWLS-MX[C]. In Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (AAMAS'06). New York,2006, 915-922.
[8]J.F. Rual, et. al, Towards a proteome-scale map of the human protein-protein interaction network[J], Nature 437:1173-1178,2005.
[9]汤进.基于图理论的图像描述与检索方法研究[D].合肥,安徽大学,2007.
[10]H.Bnnke. Recent development in graph matching[C]. In Proceeding of Conference on Pattern Recognition. Barcelona, Spain,2000.2.117-124.
[11]Jianbo Shi. J. Malik. Normalized Cuts and Image Segmentation[J]. Pattern Analysis and machine Intelligence, IEEE Transactions on.2000.22(8):888-1005.
[12]B. Luo, R.C.Wilson, E.R.Hancock. Spectral embedding of graphs[J]. Pattern Recognition.2003,36(10):2213-2223.
[13]C. Theoharatos, G. Economou, S.Fotopoulos, N.A.Laskaris. Color-based Image Retrieval using Vector Quantization and Multivariate Graph Matching[C] In Proceeding of International Conference on Image Processing(ICIP'05). London, UK,2005,535-540.
[14]Szeliski R, Zabih R, Scharstein D, et al. A comparative study of energy minimization methods for markov random fields with smoothness-based priors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(6): 1068-1080.
[15]Ford L, Fulkerson D. Flow s in Networks[M]. New Jersey:Princeton University Press,1962.
[16]R.P.W. Duin, et. al. On Euclidean corrections for non-Euclidean dissimilarities[C], Lecture Notes in Comp.Sc., vol.5342, Springer, Berlin,2008,551-561.
[17]S.Yan, D. Xu, B, Zhang, H. J. Zhang, Q. Yang, S. Lin. Graph embedding and extension:a general framework for dimensionality reduction[J]. IEEE transactions on pattern analysis and machine intelligence.29(1):40-51,2007.
[18]Seung H S, Lee D D. The manifold ways of perception [J]. Science,2000,290 (5500):2268-2269.
[19]陈省身,陈维身.微分几何讲义[M].北京：北京大学出版社,1983.
[20]罗四维,赵连伟.基于谱图理论的流形学习算法[J].计算机研究与发展,2006,43(7)：1173-1179.
[21]Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science,2000,29 (5500):2319-2323.
[22]Roweis S, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science,2000,290(5500):2323-2326.
[23]Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation,2003,15(6):1373-1396.
[24]He X, Niyogi P. Locality preserving projections[C]. In proceeding of Advances in Neural Information Processing Systems.Cambridge:MIT Press,2003:291-299.
[25]M Tnceryan, T Chorzempa. Relative Sensitivity of a Family of Closest Point Graphs in Computer Vision Applications[J]. Pattern Recognition.1991,25: 361-373.
[26]C Mauro, M Diligenti, M Gori, M. Maggini. Similarity Learning for Graph-based Image Representations [J]. Pattern Recognition Letters.2003,24(8):1115-1122.
[27]H F Zhao, Min Bong. Bin Luo. Hierarchical Shape Representation Based on Polar-Graph Spectra[C]. In Proceeding of International Conference on Intelligent Compnting (ICIC'06). Kunming,China.2006.900-905.
[28]R. Pastor-Satorras and A. Vespignani, Epidemic dynamics in finite size scale-free networks[J], Phys. Rev. E,65,035108(R),2002.
[29]Newman, M.E.J., Detecting community structure in networks[J]. The European Physical Journal B-Condensed Matter,2004.38(2):321-330.
[30]Kabiri P, Ghorbani A A. Research on Intrusion Detection and Response:A Survey[J]. International Journal of Network Security.2005,1(2):84-102.
[31]. Paul Erdos and Alfred Renyi. On the evolution of random graphs[J]. Magyar Tud. Akad. Mat. Kutato Int. Kozl.,5:17-61,1960.
[32]Bela Bollobas, Random Graphs.2nd edition [M]. Cambridge University Press, Cambridge,2001.
[33]Paul Erdos and Alfred Renyi. On random graphs I. [J]. Publ. Math. Debrecen,6: 290-297,1959.
[34]Bela Bollobas, and Thomason A. G. Threshold functions[J]. Combinatorica,1987, 7:35-38.
[35]Barabasi, A. L. Linked:The New Science of Networks[M]. Massachusetts:Persus Publishing,2002.
[36]Wattas, D. J. The'new'science of networks[J]. Annual Review of Sociology.30: 243-270.2004.
[37]戴汝为,操龙兵Internet一个开放的复杂巨系统[J].中国科学(E辑).33(4)：289-296.2003.
[38]Karonski, M. Karen, B. Singer. and Edward, R. Scheinerman. Random intersection graphs:the subgraph problem[J]. Combin. Probab. Comput.8:131-159.1999.
[39]Stephen J. Young, and Edward R. Scheinerman. Random dot product graph models for social networks[C] In Proceeding of Workshop on Algorithms and Models for the Web-Graph (WAW).2007. Lecture Notes in Computer Science 4863,138-149 2007.
[40]Nickel, Christine. Random dot product graphs:a model for social networks[D]. Ph.D. Dissertation. Johns Hopkins University, Department of Applied Mathematics and Statistics.2007.
[41]Joyce Justicz, Peter Winkler, and Edward R. Scheinerman. Random intervals [J]. American Mathematical Monthly,97:155-162,1990.
[42]Edward R. Scheinerman. Random interval graphs[J]. Combinatorica,8:357-371, 1988.
[43]Edward R. Scheinerman. An evolution of interval graphs[J]. Discrete Mathematics, 82:287-302,1990.
[44]Karen B. Singer. Random Intersection Graphs[D]. PhD dissertation, Johns HopkinsUniversity,1995.
[45]James Fill, Karen B. Singer, and Edward R. Scheinerman. Random intersection graphs when m=w(n):an equivalence theorem relating the evolution of the G(n, m, p) and G(n, p) models[J]. Random Structures and Algorithms,16:156-176, 2000.
[46]Carey E. Priebe, and David J. Marchette. Characterizing the dimensionality of a classification problem[J]. Computing Science and Statistics,32:48-62,2000.
[47]Adam Cannon, and Lenore Cowen. Approximation algorithms for the class cover problem[J]. Annals of Math, and Al.40:215-223,2004.
[48]Carey E. Priebe, David J. Marchette, Jason G. DeVinney, and Diego A. Socolinsky. Classification using class cover catch digraphs[J]. Journal of Classification,20: 3-23,2003.
[49]Jason G. DeVinney, and Carey E. Priebe. A new family of proximity graphs:Class cover catch digraphs[J]. Discrete Appl. Math.154,1975-1982.2006
[50]Elvan Ceyhan, and Carey E. Priebe. On the distribution of the domination number of a new family of parametrized random digraphs[J]. Model Assisted Statist. Appl.1:231-255.2006.
[51]John C. Wiermana, and Pengfei Xiang. A general SLLN for the one-dimensional class cover problem[J]. Statist. Probab. Lett.78:1110-1118.2008.
[52]Pengfei Xiang. Limit theory for the domination number of random class cover catch digraphs[D]. Ph.D. Dissertation. Johns Hopkins University, Department of Applied Mathematics and Statistics.2006.
[53]Diego A. Socolinsky, J. D. Neuheisel, Carey E. Priebe, Jason G. DeVinney, and David J. Marchette. Fast face detection with a boosted CCCD classifier[J]. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2003, IEEE, New York,183-191.
[54]Carey E. Priebe, Jerey L. Solka, David J. Marchette, and Ted Clark. Class cover catch digraphs for latent class discovery in gene expression monitoring by DNA microarrays[J]. Computational Statistics and Data Analysis,43D:621-632,2003.
[55]黄艳新,周春光,邹淑雪,王岩.一种求解类覆盖问题的混合算法[J].软件学报.16(4)：513-522.2006.
[56]Jin Tang, Chunyan Zhang, and Bin Luo. A New Approach to Graph Seriation[C]. In Proceedings of International Conference on Innovative Computing, Information and Control (ICICIC'06). Beijing.1,625-628.2006.
[57]David J. Marchette. Random graph for statistical pattern recognition [M].1st edition. Wiley-Interscience,2004.
[58]Nowicki K., and Snijders T. Estimation and prediction for stochastic block-structures[J]. J. Am. Stat. Assoc.96:1077-1087 2001.
[59]Albert R., and Barabasi A. L. Statistical mechanics of complex networks[J]. Rev. Mod. Phys.74,47-97 2002.
[60]Newman M.E.J. Fast algorithm for detecting community structure in networks[J]. Phys. Rev. E 69 2004.
[61]Clauset A., Newman M.E.J., and Moore C. Finding community structure in very large networks[J]. Physical Review E,70(6),2004.
[62]J. J. Daudin, Picard F., and Robin S. A mixture model for random graphs[J] Statist. Comput.18(2):151-171.2008.
[63]Edward Scheinerman. Random dot product graphs[EB/QL]. http://www.ipam.ucla. edu/schedule.aspx?pc= gss2005.
[64]Stephen J Young, E R Scheinerman. Directed Random Dot Product Graphs[J]. Internet Mathematics.2008,5(1-2):91-112
[65]Young S, Scheinerman E. Random Dot Product Graph Models for Social Networks[C]. In Proceeding of the 5th Workshop on Algorithms and Models for The Web-Graph. San Diego:University of California,2007:138-149.
[66]Bela Bollobas, Svante Janson, and Oliver Riordan. The phase transition in inhomogeneous random graphs[J]. Random Struc. Alg,31(1):3-122,2007.
[67]David J. Marchette, and Carey E. Priebe. Predicting unobserved links in incompletely observed networks [J] Computational Statistics & Data Analysis 52: 1373-1386 2008.
[68]Elizabeth Beer, James Allen Fill, Svante Janson, and Edward R. Scheinerman. On vertex, edge, and vertex-edge random graphs[J]. The Electronic Journal of Combinatorics,2011,18(1)31-35.
[69]Kraetzl M, Nickel, Scheinerman ER. Random dot product graphs:a model for social networks[D]. Baltimore, Maryland:Johns Hopkins University,2007.
[70]Hoff PD, Raftery AE, Handcock MS. Latent space approaches to social network analysis[J]. Journal of the American Statistical Association.2002,97(460): 1090-1098.
[71]Grossman J. The Erdos number project [EB/QL]. http://www.oakland.edu/enp.
[72]Gibler DM, Sarkees MR. Measuring alliances:the correlates of war formal interstate alliance dataset[J],1816-2000, Journal of Peace Research,2004,41(2): 211-222.
[73]Bollobas, B. and Riordan, O. The diameter of a scale-free random graph[J]. Combinatorica.24(1),5-34,2004.
[74]Faloutsos, M., Faloutsos, P., and Faloutsos, C., On power-law relationships of the internet topology[C]. In SIGCOMM'99:Proceedings of the conference on Applications, technologies, architectures, and protocols for camputer communication, New York, NY, USA.251-262, ACM,1999.
[75]Lakhina, A., Byers, J., Crovella, M., and Xie, P., Sampling biases in ip topology measurements[C]. In Proceeding of Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2003). IEEE, vol. 1.332-341,2003.
[76]Achlioptas, D., Clauset, A., Kempe, D., and Moore, C. On the bias of traceroute sampling:or, power-law degree distributions in regular graphs[C]. In STOC'05: Proceedings of the thirty-seventh annual ACM symposium on Theory of computing, New York, NY, USA.694-703, ACM,2005.
[77]Horn RA, Johnson CR. Matrix analysis [M]. Cambridge University Press, London, 1985.
[78]Golub GH, van Loan CF. Matrix computations[M],3rd edition. Johns Hopkins University Press, Baltimore,1996.
[79]Tucker K. Exact and asymptotic dot product representations of graphs[D]. Ph.D. Dissertation, Johns Hopkins University,2007.
[80]Rui Y, Huang T S, Chang S F. Image retrieval:current techniques,promising directions, and open issues [J]. Journal of Visual Communication and Image Representation.1999,10(1):39-62.
[81]Venters C C,Cooper M.A review of content-based image retrieval systems[EB/OL]. http://www.jtap.ac.uk/reports/htm/jtap-054.html, University of Manchester, UK. 1999.
[82]Flickner M,Sawhney H,et al. Query by image and video content:the QBIC system [J]. IEEE Computer.1995,28(9):23-32.
[83]Niblack W. The QBIC projects:querying images by content using color,texture,and shape[C]. In Proceeding on Storage and Retrieval for Image and Video Databases. SPIE.1993,19(08):173-187.
[84]Scassellati B. Retrieving images by 2D-shape:a comparison of computation methods with human perceptual judgments[C]. In Proceeding on Storage and Retrieval for Image and Video Databases. SPIE.1994,21(85):2-14.
[85]Chang N S, Fu K S. Query-by-pictorial-example[J].. In Proceedings of the 3rd International Conference on Computer Software and Applications.1979, IEEE, 288-321.
[86]Chang S F, Yan C W,Dimitroff D C,et al. An intelligent image database system[J]. IEEE Transactions on Software Engineering.1988,14:681-688.
[87]Eakins J P. Automatic image content retrieval-Are we getting anywhere?[C]. In Proceedings of the 3rd International Conference on Electronic Libraryand Visual Information Research. De Montfort University,Milton Keynes:Aslib.1996:123-135.
[88]Smeulders A W M,Worring M,Santini S,et al. Content-based image retrieval at the end of the early years[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence.2000,22(12):1349-1380.
[89]Liu Y,Zhang D S,Lu G J,et al. A survery of content-based image retrieval with high-level semantics[J]. Pattern Recognition.2007,40(1):262-282.
[90]Mori Y, Takahashi H,Oka R. Image-to-word transformation based on dividing and vector quantizing images with words[OL]. http://citeseer.ist.psu.edu/368129.html.
[91]Jeon J, Lavrenko V, Mnmatha R. Automatic image annotation and retrieval using cross-media relevance models[C]. In Proceedings of the 26th Annual Intelnational ACM SIGIR Conference on Research and Development in information Retrieval,Toronto.2003:119-126.
[92]Lavrenko V, Manmatha R, Jeon J. Amodel for learing the semantics of pictures[C] In Proceedings of the 17th International Conference on Neural Information Processing Systems, Vancouver.2003:553-560.
[93]Jin Y, Khan L, Wang L, et al. Image annotations by combining multiple evidence & WordNet[C] In Proceedings of the 13th annual ACM International Conference on Multimedia. New York:ACM,2005:706-715.
[94]Jin Y, Khan L, Prabhakaran B. Knowledge Based Image Annotation Refinement[J]. Journal of Signal Processing Systems,2010,58(3):387-406.
[95]Wang C H, Jing F, Zhang L, et al. Image annotation refinement using random walk with restarts[C] Proceeding of the 14th Annual ACM International Conference on Multimedia. New York:ACM,2006:647-650.
[96]Wang C H, Jing F, Zhang L, et al. Content-based image annotation refinement[C] In Proceeding of IEEE Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society,2007:1-8.
[97]Jia-Yu Pan, Hyung-Jeong Yang, Christos Faloutsos, et al. GCap:Graph-based Automatic Image Captioning[C]. In Proceedings of the 4th International Workshop on Multimedia Data and Document Engineering (MDDE 04), in conjunction with Computer Vision Pattern Recognition Conference (CVPR 04).2004:146-156.
[98]Witten I H, Moffat A, Bell T. Managing Gigabytes:Compressing and Indexing Documents and Images[M].Morgan Kaufmann Publishers.1999.
[99]Miller G. WordNet:A lexical database for English[J]. Communications of the ACM.1995,38(11):39-41.
[100]Pucher M. Performance evaluation of WordNet-based semantic related measure for word prediction in conversational speech[C]. In Proceedings of the 6th International Workshop on Computation Semantics.2005.
[101]Jiang J, Conrath D. Semantic similarity based on corpus statistics and lexical taxonomy[C]. In Proceedings of the International Conference on Research in Computational Linguistics.1997.129-131.
[102]Pucher M. Performance evaluation of WordNet-based semantic relatedness measures forword prediction in conversational speech[C]. In Proceedings of the 6th International Workshop on Computational Semantics.2005.38-41.
[103]Salton G, Buckley C. Term weighting approaches in automatic text retrieval[J]. Information Processing and Management,1988,24(5):513-523.
[104]David L. Olson, D. Advanced Data Mining Techniques,1st edition[M]. Berlin: Springer-Verlag,2008:137-147.
[105]Jensen D., Neville J. Data mining in social networks[C]. In the National Academy of Sciences Workshop on Dynamic Social Network Modeling and Analysis,2003.
[106]Dzeroski S. Multi-relational data mining:an introduction[C]. Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) Explorations Newsletter,2003,5(1):1-16.
[107]Dzeroski S., Lavrac N. Relational Data Mining[M]. Springer, Berlin, Germany, 2001.
[108]Deng Cai, Zheng Shao, Xiaofei He, Xifeng Yan, Jiawei Han. Community Mining from Multi-relational Networks [C]. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Porto, Portugal.2005. USA, Springer,2005.445-452.
[109]Gary William Flake, Steve Lawrence, C. Lee Giles. Efficient Identification of Web Communities[C]. In Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD-2000). Boston, MA, USA.2000. USA, ACM,2000.150-160.
[110]Ryutaro Ichise, Hideaki Takeda, Taichi Muraki, Research Community Mining with Topic Identification[C]. In Proceedings of the Information Visualization (IV'06). Baltimore,Maryland, USA,2006. IEEE Computer Society,2006.276-281.
[111]刘军.社会网络分析导论[M].北京.社会科学文献出版社.2004-12.112-175.
[112]乔少杰,唐常杰等.基于属性筛选支持向量机挖掘虚拟社团结构[J].计算机科学(增A)2005.32(7)：208-212.
[113]Vito Albino. Knowledge transfer and inter-firm relationships in industrial districts: the role of the leader firm [J]. Technovation,1999,19:53-63.
[114]Jiawei Han, Micheline Kamber. Data Mining:Concepts and Techniques[M],2nd Edition北京：机械工业出版社.2006-04：556-570.
[115]Wen-Jun Zhou, Ji-Rong Wen, Wei-Ying Ma, Hong-Jiang Zhang. A Concentric-Circle Model for Community Mining in Graph Structures [R]. Technical Report, MSR-TR-2002-123.
[116]唐常杰,刘威,温粉莲,乔少杰.社会网络分析和社团信息挖掘的三项探索：挖掘虚拟社团的结构、核心通信行为[J].计算机应用,200626(9)：2020-2023.
[117]陈烨,邵健,朱科.基于社群隐含主题挖掘和多社群信息融合的自动图像标注[J].中国图象图形学报,2010 15(6)：944-950.
[118]M. Newman. Who is the best connected scientist? a study of scientific coauthorship networks[M]. In E. Ben-Naim, H. Frauenfelder, and Z. Toroczkai. Complex Networks. Springer,2004.337-370.
[119]White S. and Smyth P., Algorithms for estimating relative importance in networks[C]. In Proceeding of International Conference on Knowledge Discovery and Data mining (ACM SIGKDD),2003. Washington, DC, USA. ACM,2003. 337-370.
[120]Givan M, Newman M. Community structure in social and biological networks[J]. Proc Natl Acad Sci USA,2002,99:7821-7826.
[121]Stoer, M., Wagner, F. A simple min-cut algorithm[J]. ACM,1997,44(4):585-591.
[122]Hagen, L., Kahng, A. New spectral methods for ratio cut partitioning and clustering [J]. IEEE Trans. Comput. Aided Des.,1992,11(9):1074-1085.
[123]Wagner, D., Wagner, F. Between min-cut and graph bisection [J]. In Proceedings of the 18th International Symposium on Mathematical Foundations of Computer Science (MFCS),1993,744-750.
[124]Ding, C. A tutorial on spectral clustering [R]. Talk presented at ICML,2004.
[125]Chung F. Spectral graph theory[M], CBMS Regional Conference Series in Mathematics vol 92. American Mathematical Society,1997.
[126]P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990.12(7):629-639.
[127]M. Proesmans, L. J. VanGool, E. Pauwcls, and A. Oosterlinck. Determination of optical flow and its discontinuities using nonlinear diffusion[C]. In Proceedings of the third European conference on Computer Vision,1994.295-304.
[128]D. Zhou, O. Bousquet, T. Lal, J.Weston, and B. Scholkopf. Learning with local and global consistency. In Advances in Neural Information Processing Systems 16, 2004.16(16):321-368.
[129]X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions[C]. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 03),2003, Washington, DC, USA. 912-919.
[130]A. D. Szlam, M. Maggioni, and R. R. Coifman[J]. Regularization on graphs with function-adapted diffusion processes. Journal of Machine Learning Research, 9:1711-1739,2008.
[131]L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples:an incremental bayesian approach tested on 101 object categories[C]. In Conference on Computer Vision and Pattern Recognition Workshop on Generative-Model Based Vision,2004.178-178.
[132]A. F. Smeaton, P. Over, and W. Kraaij. Evaluation campaigns and trecvid[C]. In Proceedings of the 8th ACM international workshop on Multimedia information retrieval (MIR 06),2006, New York,321-330.
[133]M. Naphade, J. R. Smith, J. Tesic, S.-F. Chang, W. Hsu, L. Kennedy, A. Hauptmann, and J. Curtis. Large scale concept ontology for multimedia[J]. IEEE Multimedia,2006,13(3) 86-91.
[134]M. Everingham, A. Zisserman, C. Williams, and L. V. Gool. The pascal visual object classes challenge 2006 (voc 2006) results[R]. In University of Oxford Technical Report,2006.
[135]Y. G. Jiang, C.W. Ngo, and J. Yang. Towards optimal bag-of-features for object categorization and semantic video retrieval[C]. In Proceedings of the 6th ACM international conference on Image and video retrieval (CIVR 07) 2007, New York, 494-507.
[136]T. Jebara, J. Wang, and S.-F. Chang. Graph construction and b-matching for semi-supervised learning[C]. In Proceedings of the 26th Annual International Conference on Machine Learning (ICM 09),2009, New York,441-448.
[137]Y. Aytar, O. B. Orhan, and M. Shah. Improving semantic concept detection and retrieval using contextual estimates[C]. In Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, (ICME 07),2007, Beijing, 536-539.
[138]W. Jiang, S. F. Chang, and A. Loui. Context-based concept fusion with boosted conditional random fields[C]. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP 07),2007, Honolulu, 949-952.
[139]M.-F. Weng and Y.-Y. Chuang. Multi-cue fusion for semantic video indexing[C]. In Proceedings of the 16th ACM international conference on Multimedia (MM 08), 2008, New York,71-80.
[140]G. J. Qi, X. S. Hua, Y. Rui, J. Tang, T. Mei, and H. J. Zhang. Correlative multi-label video annotation[C]. In Proceedings of the 15th ACM international conference on Multimedia (MM 07),2007, New York,17-26.
[141]Borg I, Groenen P. Modern multidimensional scaling:theory and applications[M]. Springer-Verlag.1997, New York.
[142]Cox T F, Cox M A A. Multidimensional Scaling[M]. Chapman & Hall.1994, London.
[143]Everitt B S, Dunn G. Applied multivariate data analysis[M]. Edward Arnold,1991, London.
[144]Donoho D,Grimes C. Hessian eigenmaps:Locally linear embedding techniques for high-dimensioinal data [C]. In Proceedings of the National Academy of Sciences of the United States of America,2003,100(10):5591-5596.
[145]Zhang Z Y,Zha H Y. Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignmnet [J]. SIAM Journal of Scientific Computing,2004,26 (1):313-338.
[146]J. Yang, J.-y. Yang, Why can LDA be performed in PCA transformed space? [J] Pattern Recognition,2003,36(2):563-566.
[147]A. M. Martinez, A. C. Kak, PCA versus LDA. IEEE transactions on pattern analysis and machine intelligence[J],2001 23(2):228-233.
[148]张军平.流形学习若干问题研究.王珏,周志华,周傲英.机器学习及其应用[M].北京：清华大学出版社,2006：135-169.
[149]尹峻松,肖健,周宗潭,等.非线性流形学习方法的分析和应用[J].自然科学进展,2007,17(8)：1015-1025.
[150]黄启宏,刘钊.流形学习中非线性维数约简方法概述[J].计算机应用研究,2007,24(11)：19-25.
[151]K. M. Hall. R-dimensional quadratic placement algorithm[J]. Management Science,1971,17:219-229.
[152]Weinberger K Q., Saul L K., Unsupervised learning of image manifolds by semidefinite programming [C]. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 04),2004:988-995.
[153]Weinberger K Q., Saul L K. Unsupervised learning of image manifolds by semi-defmite programming[J]. International Journal of Computer Vision,2006, 70(1):77-90.
[154]de Ridder D, Kouropteva O, Okun O, et al. Supervised locally linear embedding[C] In Proceedings of Artificial Neural Networks and Neural Information Processing, (ICANN/ICONIP 03). Lecture Notes in Computer Science, Springer 2714,2003:333-341.
[155]Geng X, Zhan D C, Zhou Z H. Supervised nonlinear dimensionality reduction for visualization and classification[J]. IEEE Transactions on Systems, Man,and Cybernetics,2005,35 (6):1098-1107.
[156]Belkin M, Niyogi P. Semi-supervised learning on Riemannian Manifolds[J]. Machine Learning,2004,56 (1):209-239.
[157]Belkin M,Niyogi P. Sindhwani V. On manifold regularization[C]. In Proceedings of the International Conference on AI and Statistics.2005:17-24.
[158]广凯,潘金贵.一种基于向量夹角的k近邻多标记文本分类算法[J].计算机科学.2008 35(4)：205-207.
[159]胡迪,陈运,杨义先,陈悦.基于支持向量机与余弦夹角法的中文网页过滤的研究与设计[J].成都信息工程学院学报,2011,26(5)：527-531.
[160]J. D. Leeuw, P. Mair. Multidimensional Scaling using Majorization:SMACOF in R[J]. Journal of Statistical Software,2009,31(3):1-30.
[161]E. Pekalska, A. Harol, R. P. W. Duin, B. Spillmann, H. Bunke. Non-Euclidean or non-metric measures can be informative[C],2006. In Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition. (SSPR/SPR), 871-880.
[162]T. Lin, H. B. Zha, Riemannian Manifold Learning[J]. IEEE transactions on pattern analysis and machine intelligence,2008.5(30):796-809.
[163]Lin, T., Zha,.H. B., Lee, S. U., Riemannian manifold learning for nonlinear dimensionality reduction [C]. In Proceedings of 11th European Conference on Computer Vision,2006, Lecture notes in computer science 3951:44-55.
[164]K. M. Carter, Raviv Raich, Alfred O. Hero Ⅲ, Spherical laplacian information maps (SLIM) for dimensionality reduction[C]. In Proceeding of IEEE Workshop on Statistical Signal Processing,2009, IEEE, New York,321-328.
[165]M.G. Genton, N. Cristianini, J.S. Taylor, R. Williamson.; Classes of kernels for machine learning:a statistics perspective[J], Journal of Machine Learning Research,2001,2:299-312.
[166]赵连伟,罗四维,赵艳敞,等.高维数据的低维嵌入及嵌入维数研究[J].软件 学报,2005,16(8)：1423-1430.
[167]孙继广,矩阵扰动分析(第二版)[M],北京.科学出版社,2001.
[168]A. Singhal, C. Buckley, M, Mitra. Pivoted document length normalization[C]. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval,1996.5(32):21-29.
[169]W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition:A Literature. Survey[J], ACM Computing Surveys,2003,35(4):399-458.
[170]D. Cai, X. He, Y. Hu, J. Han, and T. Huang, Learning a Spatially Smooth Subspace for Face Recognition[C], In Proceedings of IEEE Computer Vision and Pattern Recognition 2007 (CVPR 07).2007, Minneapolis, Minnesota, USA 1-7.
[171]Sun D., Ding. C., Luo. B., & Tang. J. Angular Decomposition[C]. In Proceeding of the 21th International Joint Conference on Artificial Intelligence (IJCAI 11). 2011, Barcelona,1505-1510.
[172]Hartigan, J., and Wong, M. A k-means clustering algorithm[J]. Applied Statistics 1979,28:100-108.
[173]Ding. C., & He. X. K-means clustering via principal component analysis[C]. In Proceeding of International Conference in Machine Learning (ICML),2004.
[174]Zhang, R.,& Rudnicky, A. A large scale clustering scheme for kernel K-means[C]. In Proceeding of 16th Intenational Conference Pattern Recognition (ICPR 02),2002, 4:289-292.
[175]D. H. Chau, S. Pandit, and C. Faloutsos, Detecting fraudulent personalities in networks of online auctioneers[C]. In Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases (PKDD'06),2006, Springer-Verlag Berlin,103-114.
[176]S. Pandit, D. Chau, S. Wang, and C. Faloutsos, Netprobe:a fast and scalable system for fraud detection in online auction networks[C]. In Proceedings of the 16th international conference on World Wide Web (WWW 07),2007, ACM, New York.201-210.
[177]N. Shrivastava, A. Majumder, and R. Rastogi, Mining (social) network graphs to detect random link attacks [C]. In Proceedings of IEEE 24th International Conference on Data Engineering (ICDE 08),2008, IEEE Computer Society Washington,486-495.
[178]Abello, J., A.L. Buchsbaum, and J. Westbrook, A functional approach to external graph algorithms[C], In Proceedings of the 6th Annual European Symposium on Algorithms.1998, Springer.332-343.
[179]Redner, S., How popular is your paper? an empirical study of the citation distribution[J]. European Physical Journal B,1998(4):131-134.
[180]Newman, M.E.J., Coauthorship networks and patterns of scientific collaboration[J]. PNAS,2004.101(1):5200-5205.
[181]Watts, D.J. and S.H. Strogatz, Collective Dynamics of Small-World Networks[J], Nature.1998.440-442.
[182]A. Bratko, B. Filipic, G Cormack, T. Lynam, and B. Zupan, Spam Filtering Using Statistical Data Compression Models[J]. Journal of Machine Learning Research, vol.7,2673-2698,2006.
[183]Z. Gyongyi, H. Garcia-Molina, and J. Pedersen, Combating web spam with trustrank[C]. In Proceedings of the 30th international conference on Very large data bases,2004, VLDB Endowment.576-587.
[184]P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman, Reputation systems [C]. Communications of the ACM,43(12),45-48,2000.
[185]L. Backstrom, C. Dwork, and J. Kleinberg, Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography[C]. In Proceedings of the 16th international conference on World Wide Web (WWW 07), 2007, ACM, New York, USA,181-190.
[186]M. Hay, G. Miklau, D. Jensen, D. Towsley, and P. Weis, Resisting structural re-identification in anonymized social networks[C]. In Proceedings of the 34th international conference on Very large data bases,2008, VLDB Endowment 1(1): 102-114.
[187]K. Liu and E. Terzi, Towards identity anonymization on graphs[C]. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (SIGMOD 08),2008. ACM, New York, USA,93-106.
[188]G. Cormode, D. Srivastava, T. Yu, and Q. Zhang, Anonymizing bipartite graph data using safe groupings[C]. In Proceedings of the 34th international conference on Very large data bases,2008, VLDB Endowment.1(1):833-844.
[189]B. Zhou and J. Pei, Preserving privacy in social networks against neighborhood attacks[C], In Proceedings of the 24th International Conference on Data Engineering, (ICDE 08),2008, Cancun, Mexico. IEEE.506-515.
[190]A. Seary and W. Richards, Spectral methods for analyzing and visualizing networks:an introduction[C] In Proceeding of National Research Council, Dynamic Social Network Modelling and Analysis:Workshop Summary and Papers, 209-228,2003.
[191]G. W. Stewart and J. Sun, Matrix Perturbation Theory[M]. Academic Press,1990.
[192]Latouche, Guy; Ramaswami, Vaidyanathan. Introduction to matrix analytic methods in stochastic modeling (1st ed.)[M], Philadelphia, PA:Society for Industrial and Applied Mathematics 1999.
[193]Fan Chung, Linyuan Lu, and Van Vu. Eigenvalues of random power law graphs[J]. Annals of Combinatorics,7:21-33,2003.
[194]A. Niklasson, V. Weber and Matt Challacombe, Nonorthogonal density-matrix perturbation theory[J]. Journal of Chemical Physics,123(4) 044107 2005.
[195]X. Ying and X. Wu, On randomness measures for social networks[C] In Proceedings of the SIAM International Conference on Data Mining (SDM 09), 2009. Sparks, Nevada, USA,709-720.
[196]X. Ying and X. Wu, A Spectrum-based Framework for Quantifying Randomness of Social Networks[J]. IEEE Transactions on Knowledge and Data Engineering, 201123(12) 1842-1856.
[197]L. Adamic and N. Glance, The political blogosphere and the 2004 us election: divided they blog[C]. In Proceedings of the 3rd international workshop on Link discovery (LinkKDD 05'),2005. ACM, New York. USA.36-43.
[198]S. J. Press, Linear combinations of non-central chi-square variates [J]. The Annals of Mathematical Statistics,37(2):480-487,1966.
[199]Fukunaga, Keinosuke; Larry D. Hostetler. The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory[J].197521(1):32-40.
[200]M. Charikar, Greedy approximation algorithms for finding dense components in a graph [C]. In Proceeding of the 3rd International Workshop on Approximation Algorithms for Combinatorial Optimization (APPROX'00),2000, Springer-Verlag London, UK 84-95.
[201]Web spam challenge [EB/OL]. http://webspam.lip6.fr/wiki/pmwiki.php,2007.
[202]J. Newsome, E. Shi, D. Song, and A. Perrig, The sybil attack in sensor networks: analysis & defenses[C]. In Proceedings of the 3rd international symposium on Information processing in sensor networks (IPSN '04) 2004, ACM, New York. USA.259-268.
[2]F. Rosenblatt, The perceptron:a probabilistic model for information storage and organization in the brain, Psychological Review,65:386-408,1958.
[3]N.J. Nilsson, Learning Machines[M], McGraw-Hill, New York,1965.
[4]R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification (2nd edition) [M], Wiley Interscience, New York,2001.
[5]D.L. Donoho, High-Dimensional Data Analysis:The Curses and Blessings of Dimensionality[C], In Proceeding of International Conference of Mathematicians (AMS). Challenges of the 21st Century,2000.
[6]J.Scott. Social network analysis, Sociology the journal of the British sociological association,1(22):109-127,1994.
[7]M.Klusch, B. Fries, K, Sycara. Automated semantic web service discovery with OWLS-MX[C]. In Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (AAMAS'06). New York,2006, 915-922.
[8]J.F. Rual, et. al, Towards a proteome-scale map of the human protein-protein interaction network[J], Nature 437:1173-1178,2005.
[9]汤进.基于图理论的图像描述与检索方法研究[D].合肥,安徽大学,2007.
[10]H.Bnnke. Recent development in graph matching[C]. In Proceeding of Conference on Pattern Recognition. Barcelona, Spain,2000.2.117-124.
[11]Jianbo Shi. J. Malik. Normalized Cuts and Image Segmentation[J]. Pattern Analysis and machine Intelligence, IEEE Transactions on.2000.22(8):888-1005.
[12]B. Luo, R.C.Wilson, E.R.Hancock. Spectral embedding of graphs[J]. Pattern Recognition.2003,36(10):2213-2223.
[13]C. Theoharatos, G. Economou, S.Fotopoulos, N.A.Laskaris. Color-based Image Retrieval using Vector Quantization and Multivariate Graph Matching[C] In Proceeding of International Conference on Image Processing(ICIP'05). London, UK,2005,535-540.
[14]Szeliski R, Zabih R, Scharstein D, et al. A comparative study of energy minimization methods for markov random fields with smoothness-based priors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(6): 1068-1080.
[15]Ford L, Fulkerson D. Flow s in Networks[M]. New Jersey:Princeton University Press,1962.
[16]R.P.W. Duin, et. al. On Euclidean corrections for non-Euclidean dissimilarities[C], Lecture Notes in Comp.Sc., vol.5342, Springer, Berlin,2008,551-561.
[17]S.Yan, D. Xu, B, Zhang, H. J. Zhang, Q. Yang, S. Lin. Graph embedding and extension:a general framework for dimensionality reduction[J]. IEEE transactions on pattern analysis and machine intelligence.29(1):40-51,2007.
[18]Seung H S, Lee D D. The manifold ways of perception [J]. Science,2000,290 (5500):2268-2269.
[19]陈省身,陈维身.微分几何讲义[M].北京：北京大学出版社,1983.
[20]罗四维,赵连伟.基于谱图理论的流形学习算法[J].计算机研究与发展,2006,43(7)：1173-1179.
[21]Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science,2000,29 (5500):2319-2323.
[22]Roweis S, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science,2000,290(5500):2323-2326.
[23]Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation,2003,15(6):1373-1396.
[24]He X, Niyogi P. Locality preserving projections[C]. In proceeding of Advances in Neural Information Processing Systems.Cambridge:MIT Press,2003:291-299.
[25]M Tnceryan, T Chorzempa. Relative Sensitivity of a Family of Closest Point Graphs in Computer Vision Applications[J]. Pattern Recognition.1991,25: 361-373.
[26]C Mauro, M Diligenti, M Gori, M. Maggini. Similarity Learning for Graph-based Image Representations [J]. Pattern Recognition Letters.2003,24(8):1115-1122.
[27]H F Zhao, Min Bong. Bin Luo. Hierarchical Shape Representation Based on Polar-Graph Spectra[C]. In Proceeding of International Conference on Intelligent Compnting (ICIC'06). Kunming,China.2006.900-905.
[28]R. Pastor-Satorras and A. Vespignani, Epidemic dynamics in finite size scale-free networks[J], Phys. Rev. E,65,035108(R),2002.
[29]Newman, M.E.J., Detecting community structure in networks[J]. The European Physical Journal B-Condensed Matter,2004.38(2):321-330.
[30]Kabiri P, Ghorbani A A. Research on Intrusion Detection and Response:A Survey[J]. International Journal of Network Security.2005,1(2):84-102.
[31]. Paul Erdos and Alfred Renyi. On the evolution of random graphs[J]. Magyar Tud. Akad. Mat. Kutato Int. Kozl.,5:17-61,1960.
[32]Bela Bollobas, Random Graphs.2nd edition [M]. Cambridge University Press, Cambridge,2001.
[33]Paul Erdos and Alfred Renyi. On random graphs I. [J]. Publ. Math. Debrecen,6: 290-297,1959.
[34]Bela Bollobas, and Thomason A. G. Threshold functions[J]. Combinatorica,1987, 7:35-38.
[35]Barabasi, A. L. Linked:The New Science of Networks[M]. Massachusetts:Persus Publishing,2002.
[36]Wattas, D. J. The'new'science of networks[J]. Annual Review of Sociology.30: 243-270.2004.
[37]戴汝为,操龙兵Internet一个开放的复杂巨系统[J].中国科学(E辑).33(4)：289-296.2003.
[38]Karonski, M. Karen, B. Singer. and Edward, R. Scheinerman. Random intersection graphs:the subgraph problem[J]. Combin. Probab. Comput.8:131-159.1999.
[39]Stephen J. Young, and Edward R. Scheinerman. Random dot product graph models for social networks[C] In Proceeding of Workshop on Algorithms and Models for the Web-Graph (WAW).2007. Lecture Notes in Computer Science 4863,138-149 2007.
[40]Nickel, Christine. Random dot product graphs:a model for social networks[D]. Ph.D. Dissertation. Johns Hopkins University, Department of Applied Mathematics and Statistics.2007.
[41]Joyce Justicz, Peter Winkler, and Edward R. Scheinerman. Random intervals [J]. American Mathematical Monthly,97:155-162,1990.
[42]Edward R. Scheinerman. Random interval graphs[J]. Combinatorica,8:357-371, 1988.
[43]Edward R. Scheinerman. An evolution of interval graphs[J]. Discrete Mathematics, 82:287-302,1990.
[44]Karen B. Singer. Random Intersection Graphs[D]. PhD dissertation, Johns HopkinsUniversity,1995.
[45]James Fill, Karen B. Singer, and Edward R. Scheinerman. Random intersection graphs when m=w(n):an equivalence theorem relating the evolution of the G(n, m, p) and G(n, p) models[J]. Random Structures and Algorithms,16:156-176, 2000.
[46]Carey E. Priebe, and David J. Marchette. Characterizing the dimensionality of a classification problem[J]. Computing Science and Statistics,32:48-62,2000.
[47]Adam Cannon, and Lenore Cowen. Approximation algorithms for the class cover problem[J]. Annals of Math, and Al.40:215-223,2004.
[48]Carey E. Priebe, David J. Marchette, Jason G. DeVinney, and Diego A. Socolinsky. Classification using class cover catch digraphs[J]. Journal of Classification,20: 3-23,2003.
[49]Jason G. DeVinney, and Carey E. Priebe. A new family of proximity graphs:Class cover catch digraphs[J]. Discrete Appl. Math.154,1975-1982.2006
[50]Elvan Ceyhan, and Carey E. Priebe. On the distribution of the domination number of a new family of parametrized random digraphs[J]. Model Assisted Statist. Appl.1:231-255.2006.
[51]John C. Wiermana, and Pengfei Xiang. A general SLLN for the one-dimensional class cover problem[J]. Statist. Probab. Lett.78:1110-1118.2008.
[52]Pengfei Xiang. Limit theory for the domination number of random class cover catch digraphs[D]. Ph.D. Dissertation. Johns Hopkins University, Department of Applied Mathematics and Statistics.2006.
[53]Diego A. Socolinsky, J. D. Neuheisel, Carey E. Priebe, Jason G. DeVinney, and David J. Marchette. Fast face detection with a boosted CCCD classifier[J]. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2003, IEEE, New York,183-191.
[54]Carey E. Priebe, Jerey L. Solka, David J. Marchette, and Ted Clark. Class cover catch digraphs for latent class discovery in gene expression monitoring by DNA microarrays[J]. Computational Statistics and Data Analysis,43D:621-632,2003.
[55]黄艳新,周春光,邹淑雪,王岩.一种求解类覆盖问题的混合算法[J].软件学报.16(4)：513-522.2006.
[56]Jin Tang, Chunyan Zhang, and Bin Luo. A New Approach to Graph Seriation[C]. In Proceedings of International Conference on Innovative Computing, Information and Control (ICICIC'06). Beijing.1,625-628.2006.
[57]David J. Marchette. Random graph for statistical pattern recognition [M].1st edition. Wiley-Interscience,2004.
[58]Nowicki K., and Snijders T. Estimation and prediction for stochastic block-structures[J]. J. Am. Stat. Assoc.96:1077-1087 2001.
[59]Albert R., and Barabasi A. L. Statistical mechanics of complex networks[J]. Rev. Mod. Phys.74,47-97 2002.
[60]Newman M.E.J. Fast algorithm for detecting community structure in networks[J]. Phys. Rev. E 69 2004.
[61]Clauset A., Newman M.E.J., and Moore C. Finding community structure in very large networks[J]. Physical Review E,70(6),2004.
[62]J. J. Daudin, Picard F., and Robin S. A mixture model for random graphs[J] Statist. Comput.18(2):151-171.2008.
[63]Edward Scheinerman. Random dot product graphs[EB/QL]. http://www.ipam.ucla. edu/schedule.aspx?pc= gss2005.
[64]Stephen J Young, E R Scheinerman. Directed Random Dot Product Graphs[J]. Internet Mathematics.2008,5(1-2):91-112
[65]Young S, Scheinerman E. Random Dot Product Graph Models for Social Networks[C]. In Proceeding of the 5th Workshop on Algorithms and Models for The Web-Graph. San Diego:University of California,2007:138-149.
[66]Bela Bollobas, Svante Janson, and Oliver Riordan. The phase transition in inhomogeneous random graphs[J]. Random Struc. Alg,31(1):3-122,2007.
[67]David J. Marchette, and Carey E. Priebe. Predicting unobserved links in incompletely observed networks [J] Computational Statistics & Data Analysis 52: 1373-1386 2008.
[68]Elizabeth Beer, James Allen Fill, Svante Janson, and Edward R. Scheinerman. On vertex, edge, and vertex-edge random graphs[J]. The Electronic Journal of Combinatorics,2011,18(1)31-35.
[69]Kraetzl M, Nickel, Scheinerman ER. Random dot product graphs:a model for social networks[D]. Baltimore, Maryland:Johns Hopkins University,2007.
[70]Hoff PD, Raftery AE, Handcock MS. Latent space approaches to social network analysis[J]. Journal of the American Statistical Association.2002,97(460): 1090-1098.
[71]Grossman J. The Erdos number project [EB/QL]. http://www.oakland.edu/enp.
[72]Gibler DM, Sarkees MR. Measuring alliances:the correlates of war formal interstate alliance dataset[J],1816-2000, Journal of Peace Research,2004,41(2): 211-222.
[73]Bollobas, B. and Riordan, O. The diameter of a scale-free random graph[J]. Combinatorica.24(1),5-34,2004.
[74]Faloutsos, M., Faloutsos, P., and Faloutsos, C., On power-law relationships of the internet topology[C]. In SIGCOMM'99:Proceedings of the conference on Applications, technologies, architectures, and protocols for camputer communication, New York, NY, USA.251-262, ACM,1999.
[75]Lakhina, A., Byers, J., Crovella, M., and Xie, P., Sampling biases in ip topology measurements[C]. In Proceeding of Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2003). IEEE, vol. 1.332-341,2003.
[76]Achlioptas, D., Clauset, A., Kempe, D., and Moore, C. On the bias of traceroute sampling:or, power-law degree distributions in regular graphs[C]. In STOC'05: Proceedings of the thirty-seventh annual ACM symposium on Theory of computing, New York, NY, USA.694-703, ACM,2005.
[77]Horn RA, Johnson CR. Matrix analysis [M]. Cambridge University Press, London, 1985.
[78]Golub GH, van Loan CF. Matrix computations[M],3rd edition. Johns Hopkins University Press, Baltimore,1996.
[79]Tucker K. Exact and asymptotic dot product representations of graphs[D]. Ph.D. Dissertation, Johns Hopkins University,2007.
[80]Rui Y, Huang T S, Chang S F. Image retrieval:current techniques,promising directions, and open issues [J]. Journal of Visual Communication and Image Representation.1999,10(1):39-62.
[81]Venters C C,Cooper M.A review of content-based image retrieval systems[EB/OL]. http://www.jtap.ac.uk/reports/htm/jtap-054.html, University of Manchester, UK. 1999.
[82]Flickner M,Sawhney H,et al. Query by image and video content:the QBIC system [J]. IEEE Computer.1995,28(9):23-32.
[83]Niblack W. The QBIC projects:querying images by content using color,texture,and shape[C]. In Proceeding on Storage and Retrieval for Image and Video Databases. SPIE.1993,19(08):173-187.
[84]Scassellati B. Retrieving images by 2D-shape:a comparison of computation methods with human perceptual judgments[C]. In Proceeding on Storage and Retrieval for Image and Video Databases. SPIE.1994,21(85):2-14.
[85]Chang N S, Fu K S. Query-by-pictorial-example[J].. In Proceedings of the 3rd International Conference on Computer Software and Applications.1979, IEEE, 288-321.
[86]Chang S F, Yan C W,Dimitroff D C,et al. An intelligent image database system[J]. IEEE Transactions on Software Engineering.1988,14:681-688.
[87]Eakins J P. Automatic image content retrieval-Are we getting anywhere?[C]. In Proceedings of the 3rd International Conference on Electronic Libraryand Visual Information Research. De Montfort University,Milton Keynes:Aslib.1996:123-135.
[88]Smeulders A W M,Worring M,Santini S,et al. Content-based image retrieval at the end of the early years[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence.2000,22(12):1349-1380.
[89]Liu Y,Zhang D S,Lu G J,et al. A survery of content-based image retrieval with high-level semantics[J]. Pattern Recognition.2007,40(1):262-282.
[90]Mori Y, Takahashi H,Oka R. Image-to-word transformation based on dividing and vector quantizing images with words[OL]. http://citeseer.ist.psu.edu/368129.html.
[91]Jeon J, Lavrenko V, Mnmatha R. Automatic image annotation and retrieval using cross-media relevance models[C]. In Proceedings of the 26th Annual Intelnational ACM SIGIR Conference on Research and Development in information Retrieval,Toronto.2003:119-126.
[92]Lavrenko V, Manmatha R, Jeon J. Amodel for learing the semantics of pictures[C] In Proceedings of the 17th International Conference on Neural Information Processing Systems, Vancouver.2003:553-560.
[93]Jin Y, Khan L, Wang L, et al. Image annotations by combining multiple evidence & WordNet[C] In Proceedings of the 13th annual ACM International Conference on Multimedia. New York:ACM,2005:706-715.
[94]Jin Y, Khan L, Prabhakaran B. Knowledge Based Image Annotation Refinement[J]. Journal of Signal Processing Systems,2010,58(3):387-406.
[95]Wang C H, Jing F, Zhang L, et al. Image annotation refinement using random walk with restarts[C] Proceeding of the 14th Annual ACM International Conference on Multimedia. New York:ACM,2006:647-650.
[96]Wang C H, Jing F, Zhang L, et al. Content-based image annotation refinement[C] In Proceeding of IEEE Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society,2007:1-8.
[97]Jia-Yu Pan, Hyung-Jeong Yang, Christos Faloutsos, et al. GCap:Graph-based Automatic Image Captioning[C]. In Proceedings of the 4th International Workshop on Multimedia Data and Document Engineering (MDDE 04), in conjunction with Computer Vision Pattern Recognition Conference (CVPR 04).2004:146-156.
[98]Witten I H, Moffat A, Bell T. Managing Gigabytes:Compressing and Indexing Documents and Images[M].Morgan Kaufmann Publishers.1999.
[99]Miller G. WordNet:A lexical database for English[J]. Communications of the ACM.1995,38(11):39-41.
[100]Pucher M. Performance evaluation of WordNet-based semantic related measure for word prediction in conversational speech[C]. In Proceedings of the 6th International Workshop on Computation Semantics.2005.
[101]Jiang J, Conrath D. Semantic similarity based on corpus statistics and lexical taxonomy[C]. In Proceedings of the International Conference on Research in Computational Linguistics.1997.129-131.
[102]Pucher M. Performance evaluation of WordNet-based semantic relatedness measures forword prediction in conversational speech[C]. In Proceedings of the 6th International Workshop on Computational Semantics.2005.38-41.
[103]Salton G, Buckley C. Term weighting approaches in automatic text retrieval[J]. Information Processing and Management,1988,24(5):513-523.
[104]David L. Olson, D. Advanced Data Mining Techniques,1st edition[M]. Berlin: Springer-Verlag,2008:137-147.
[105]Jensen D., Neville J. Data mining in social networks[C]. In the National Academy of Sciences Workshop on Dynamic Social Network Modeling and Analysis,2003.
[106]Dzeroski S. Multi-relational data mining:an introduction[C]. Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) Explorations Newsletter,2003,5(1):1-16.
[107]Dzeroski S., Lavrac N. Relational Data Mining[M]. Springer, Berlin, Germany, 2001.
[108]Deng Cai, Zheng Shao, Xiaofei He, Xifeng Yan, Jiawei Han. Community Mining from Multi-relational Networks [C]. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Porto, Portugal.2005. USA, Springer,2005.445-452.
[109]Gary William Flake, Steve Lawrence, C. Lee Giles. Efficient Identification of Web Communities[C]. In Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD-2000). Boston, MA, USA.2000. USA, ACM,2000.150-160.
[110]Ryutaro Ichise, Hideaki Takeda, Taichi Muraki, Research Community Mining with Topic Identification[C]. In Proceedings of the Information Visualization (IV'06). Baltimore,Maryland, USA,2006. IEEE Computer Society,2006.276-281.
[111]刘军.社会网络分析导论[M].北京.社会科学文献出版社.2004-12.112-175.
[112]乔少杰,唐常杰等.基于属性筛选支持向量机挖掘虚拟社团结构[J].计算机科学(增A)2005.32(7)：208-212.
[113]Vito Albino. Knowledge transfer and inter-firm relationships in industrial districts: the role of the leader firm [J]. Technovation,1999,19:53-63.
[114]Jiawei Han, Micheline Kamber. Data Mining:Concepts and Techniques[M],2nd Edition北京：机械工业出版社.2006-04：556-570.
[115]Wen-Jun Zhou, Ji-Rong Wen, Wei-Ying Ma, Hong-Jiang Zhang. A Concentric-Circle Model for Community Mining in Graph Structures [R]. Technical Report, MSR-TR-2002-123.
[116]唐常杰,刘威,温粉莲,乔少杰.社会网络分析和社团信息挖掘的三项探索：挖掘虚拟社团的结构、核心通信行为[J].计算机应用,200626(9)：2020-2023.
[117]陈烨,邵健,朱科.基于社群隐含主题挖掘和多社群信息融合的自动图像标注[J].中国图象图形学报,2010 15(6)：944-950.
[118]M. Newman. Who is the best connected scientist? a study of scientific coauthorship networks[M]. In E. Ben-Naim, H. Frauenfelder, and Z. Toroczkai. Complex Networks. Springer,2004.337-370.
[119]White S. and Smyth P., Algorithms for estimating relative importance in networks[C]. In Proceeding of International Conference on Knowledge Discovery and Data mining (ACM SIGKDD),2003. Washington, DC, USA. ACM,2003. 337-370.
[120]Givan M, Newman M. Community structure in social and biological networks[J]. Proc Natl Acad Sci USA,2002,99:7821-7826.
[121]Stoer, M., Wagner, F. A simple min-cut algorithm[J]. ACM,1997,44(4):585-591.
[122]Hagen, L., Kahng, A. New spectral methods for ratio cut partitioning and clustering [J]. IEEE Trans. Comput. Aided Des.,1992,11(9):1074-1085.
[123]Wagner, D., Wagner, F. Between min-cut and graph bisection [J]. In Proceedings of the 18th International Symposium on Mathematical Foundations of Computer Science (MFCS),1993,744-750.
[124]Ding, C. A tutorial on spectral clustering [R]. Talk presented at ICML,2004.
[125]Chung F. Spectral graph theory[M], CBMS Regional Conference Series in Mathematics vol 92. American Mathematical Society,1997.
[126]P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990.12(7):629-639.
[127]M. Proesmans, L. J. VanGool, E. Pauwcls, and A. Oosterlinck. Determination of optical flow and its discontinuities using nonlinear diffusion[C]. In Proceedings of the third European conference on Computer Vision,1994.295-304.
[128]D. Zhou, O. Bousquet, T. Lal, J.Weston, and B. Scholkopf. Learning with local and global consistency. In Advances in Neural Information Processing Systems 16, 2004.16(16):321-368.
[129]X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions[C]. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 03),2003, Washington, DC, USA. 912-919.
[130]A. D. Szlam, M. Maggioni, and R. R. Coifman[J]. Regularization on graphs with function-adapted diffusion processes. Journal of Machine Learning Research, 9:1711-1739,2008.
[131]L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples:an incremental bayesian approach tested on 101 object categories[C]. In Conference on Computer Vision and Pattern Recognition Workshop on Generative-Model Based Vision,2004.178-178.
[132]A. F. Smeaton, P. Over, and W. Kraaij. Evaluation campaigns and trecvid[C]. In Proceedings of the 8th ACM international workshop on Multimedia information retrieval (MIR 06),2006, New York,321-330.
[133]M. Naphade, J. R. Smith, J. Tesic, S.-F. Chang, W. Hsu, L. Kennedy, A. Hauptmann, and J. Curtis. Large scale concept ontology for multimedia[J]. IEEE Multimedia,2006,13(3) 86-91.
[134]M. Everingham, A. Zisserman, C. Williams, and L. V. Gool. The pascal visual object classes challenge 2006 (voc 2006) results[R]. In University of Oxford Technical Report,2006.
[135]Y. G. Jiang, C.W. Ngo, and J. Yang. Towards optimal bag-of-features for object categorization and semantic video retrieval[C]. In Proceedings of the 6th ACM international conference on Image and video retrieval (CIVR 07) 2007, New York, 494-507.
[136]T. Jebara, J. Wang, and S.-F. Chang. Graph construction and b-matching for semi-supervised learning[C]. In Proceedings of the 26th Annual International Conference on Machine Learning (ICM 09),2009, New York,441-448.
[137]Y. Aytar, O. B. Orhan, and M. Shah. Improving semantic concept detection and retrieval using contextual estimates[C]. In Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, (ICME 07),2007, Beijing, 536-539.
[138]W. Jiang, S. F. Chang, and A. Loui. Context-based concept fusion with boosted conditional random fields[C]. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP 07),2007, Honolulu, 949-952.
[139]M.-F. Weng and Y.-Y. Chuang. Multi-cue fusion for semantic video indexing[C]. In Proceedings of the 16th ACM international conference on Multimedia (MM 08), 2008, New York,71-80.
[140]G. J. Qi, X. S. Hua, Y. Rui, J. Tang, T. Mei, and H. J. Zhang. Correlative multi-label video annotation[C]. In Proceedings of the 15th ACM international conference on Multimedia (MM 07),2007, New York,17-26.
[141]Borg I, Groenen P. Modern multidimensional scaling:theory and applications[M]. Springer-Verlag.1997, New York.
[142]Cox T F, Cox M A A. Multidimensional Scaling[M]. Chapman & Hall.1994, London.
[143]Everitt B S, Dunn G. Applied multivariate data analysis[M]. Edward Arnold,1991, London.
[144]Donoho D,Grimes C. Hessian eigenmaps:Locally linear embedding techniques for high-dimensioinal data [C]. In Proceedings of the National Academy of Sciences of the United States of America,2003,100(10):5591-5596.
[145]Zhang Z Y,Zha H Y. Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignmnet [J]. SIAM Journal of Scientific Computing,2004,26 (1):313-338.
[146]J. Yang, J.-y. Yang, Why can LDA be performed in PCA transformed space? [J] Pattern Recognition,2003,36(2):563-566.
[147]A. M. Martinez, A. C. Kak, PCA versus LDA. IEEE transactions on pattern analysis and machine intelligence[J],2001 23(2):228-233.
[148]张军平.流形学习若干问题研究.王珏,周志华,周傲英.机器学习及其应用[M].北京：清华大学出版社,2006：135-169.
[149]尹峻松,肖健,周宗潭,等.非线性流形学习方法的分析和应用[J].自然科学进展,2007,17(8)：1015-1025.
[150]黄启宏,刘钊.流形学习中非线性维数约简方法概述[J].计算机应用研究,2007,24(11)：19-25.
[151]K. M. Hall. R-dimensional quadratic placement algorithm[J]. Management Science,1971,17:219-229.
[152]Weinberger K Q., Saul L K., Unsupervised learning of image manifolds by semidefinite programming [C]. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 04),2004:988-995.
[153]Weinberger K Q., Saul L K. Unsupervised learning of image manifolds by semi-defmite programming[J]. International Journal of Computer Vision,2006, 70(1):77-90.
[154]de Ridder D, Kouropteva O, Okun O, et al. Supervised locally linear embedding[C] In Proceedings of Artificial Neural Networks and Neural Information Processing, (ICANN/ICONIP 03). Lecture Notes in Computer Science, Springer 2714,2003:333-341.
[155]Geng X, Zhan D C, Zhou Z H. Supervised nonlinear dimensionality reduction for visualization and classification[J]. IEEE Transactions on Systems, Man,and Cybernetics,2005,35 (6):1098-1107.
[156]Belkin M, Niyogi P. Semi-supervised learning on Riemannian Manifolds[J]. Machine Learning,2004,56 (1):209-239.
[157]Belkin M,Niyogi P. Sindhwani V. On manifold regularization[C]. In Proceedings of the International Conference on AI and Statistics.2005:17-24.
[158]广凯,潘金贵.一种基于向量夹角的k近邻多标记文本分类算法[J].计算机科学.2008 35(4)：205-207.
[159]胡迪,陈运,杨义先,陈悦.基于支持向量机与余弦夹角法的中文网页过滤的研究与设计[J].成都信息工程学院学报,2011,26(5)：527-531.
[160]J. D. Leeuw, P. Mair. Multidimensional Scaling using Majorization:SMACOF in R[J]. Journal of Statistical Software,2009,31(3):1-30.
[161]E. Pekalska, A. Harol, R. P. W. Duin, B. Spillmann, H. Bunke. Non-Euclidean or non-metric measures can be informative[C],2006. In Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition. (SSPR/SPR), 871-880.
[162]T. Lin, H. B. Zha, Riemannian Manifold Learning[J]. IEEE transactions on pattern analysis and machine intelligence,2008.5(30):796-809.
[163]Lin, T., Zha,.H. B., Lee, S. U., Riemannian manifold learning for nonlinear dimensionality reduction [C]. In Proceedings of 11th European Conference on Computer Vision,2006, Lecture notes in computer science 3951:44-55.
[164]K. M. Carter, Raviv Raich, Alfred O. Hero Ⅲ, Spherical laplacian information maps (SLIM) for dimensionality reduction[C]. In Proceeding of IEEE Workshop on Statistical Signal Processing,2009, IEEE, New York,321-328.
[165]M.G. Genton, N. Cristianini, J.S. Taylor, R. Williamson.; Classes of kernels for machine learning:a statistics perspective[J], Journal of Machine Learning Research,2001,2:299-312.
[166]赵连伟,罗四维,赵艳敞,等.高维数据的低维嵌入及嵌入维数研究[J].软件 学报,2005,16(8)：1423-1430.
[167]孙继广,矩阵扰动分析(第二版)[M],北京.科学出版社,2001.
[168]A. Singhal, C. Buckley, M, Mitra. Pivoted document length normalization[C]. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval,1996.5(32):21-29.
[169]W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition:A Literature. Survey[J], ACM Computing Surveys,2003,35(4):399-458.
[170]D. Cai, X. He, Y. Hu, J. Han, and T. Huang, Learning a Spatially Smooth Subspace for Face Recognition[C], In Proceedings of IEEE Computer Vision and Pattern Recognition 2007 (CVPR 07).2007, Minneapolis, Minnesota, USA 1-7.
[171]Sun D., Ding. C., Luo. B., & Tang. J. Angular Decomposition[C]. In Proceeding of the 21th International Joint Conference on Artificial Intelligence (IJCAI 11). 2011, Barcelona,1505-1510.
[172]Hartigan, J., and Wong, M. A k-means clustering algorithm[J]. Applied Statistics 1979,28:100-108.
[173]Ding. C., & He. X. K-means clustering via principal component analysis[C]. In Proceeding of International Conference in Machine Learning (ICML),2004.
[174]Zhang, R.,& Rudnicky, A. A large scale clustering scheme for kernel K-means[C]. In Proceeding of 16th Intenational Conference Pattern Recognition (ICPR 02),2002, 4:289-292.
[175]D. H. Chau, S. Pandit, and C. Faloutsos, Detecting fraudulent personalities in networks of online auctioneers[C]. In Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases (PKDD'06),2006, Springer-Verlag Berlin,103-114.
[176]S. Pandit, D. Chau, S. Wang, and C. Faloutsos, Netprobe:a fast and scalable system for fraud detection in online auction networks[C]. In Proceedings of the 16th international conference on World Wide Web (WWW 07),2007, ACM, New York.201-210.
[177]N. Shrivastava, A. Majumder, and R. Rastogi, Mining (social) network graphs to detect random link attacks [C]. In Proceedings of IEEE 24th International Conference on Data Engineering (ICDE 08),2008, IEEE Computer Society Washington,486-495.
[178]Abello, J., A.L. Buchsbaum, and J. Westbrook, A functional approach to external graph algorithms[C], In Proceedings of the 6th Annual European Symposium on Algorithms.1998, Springer.332-343.
[179]Redner, S., How popular is your paper? an empirical study of the citation distribution[J]. European Physical Journal B,1998(4):131-134.
[180]Newman, M.E.J., Coauthorship networks and patterns of scientific collaboration[J]. PNAS,2004.101(1):5200-5205.
[181]Watts, D.J. and S.H. Strogatz, Collective Dynamics of Small-World Networks[J], Nature.1998.440-442.
[182]A. Bratko, B. Filipic, G Cormack, T. Lynam, and B. Zupan, Spam Filtering Using Statistical Data Compression Models[J]. Journal of Machine Learning Research, vol.7,2673-2698,2006.
[183]Z. Gyongyi, H. Garcia-Molina, and J. Pedersen, Combating web spam with trustrank[C]. In Proceedings of the 30th international conference on Very large data bases,2004, VLDB Endowment.576-587.
[184]P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman, Reputation systems [C]. Communications of the ACM,43(12),45-48,2000.
[185]L. Backstrom, C. Dwork, and J. Kleinberg, Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography[C]. In Proceedings of the 16th international conference on World Wide Web (WWW 07), 2007, ACM, New York, USA,181-190.
[186]M. Hay, G. Miklau, D. Jensen, D. Towsley, and P. Weis, Resisting structural re-identification in anonymized social networks[C]. In Proceedings of the 34th international conference on Very large data bases,2008, VLDB Endowment 1(1): 102-114.
[187]K. Liu and E. Terzi, Towards identity anonymization on graphs[C]. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (SIGMOD 08),2008. ACM, New York, USA,93-106.
[188]G. Cormode, D. Srivastava, T. Yu, and Q. Zhang, Anonymizing bipartite graph data using safe groupings[C]. In Proceedings of the 34th international conference on Very large data bases,2008, VLDB Endowment.1(1):833-844.
[189]B. Zhou and J. Pei, Preserving privacy in social networks against neighborhood attacks[C], In Proceedings of the 24th International Conference on Data Engineering, (ICDE 08),2008, Cancun, Mexico. IEEE.506-515.
[190]A. Seary and W. Richards, Spectral methods for analyzing and visualizing networks:an introduction[C] In Proceeding of National Research Council, Dynamic Social Network Modelling and Analysis:Workshop Summary and Papers, 209-228,2003.
[191]G. W. Stewart and J. Sun, Matrix Perturbation Theory[M]. Academic Press,1990.
[192]Latouche, Guy; Ramaswami, Vaidyanathan. Introduction to matrix analytic methods in stochastic modeling (1st ed.)[M], Philadelphia, PA:Society for Industrial and Applied Mathematics 1999.
[193]Fan Chung, Linyuan Lu, and Van Vu. Eigenvalues of random power law graphs[J]. Annals of Combinatorics,7:21-33,2003.
[194]A. Niklasson, V. Weber and Matt Challacombe, Nonorthogonal density-matrix perturbation theory[J]. Journal of Chemical Physics,123(4) 044107 2005.
[195]X. Ying and X. Wu, On randomness measures for social networks[C] In Proceedings of the SIAM International Conference on Data Mining (SDM 09), 2009. Sparks, Nevada, USA,709-720.
[196]X. Ying and X. Wu, A Spectrum-based Framework for Quantifying Randomness of Social Networks[J]. IEEE Transactions on Knowledge and Data Engineering, 201123(12) 1842-1856.
[197]L. Adamic and N. Glance, The political blogosphere and the 2004 us election: divided they blog[C]. In Proceedings of the 3rd international workshop on Link discovery (LinkKDD 05'),2005. ACM, New York. USA.36-43.
[198]S. J. Press, Linear combinations of non-central chi-square variates [J]. The Annals of Mathematical Statistics,37(2):480-487,1966.
[199]Fukunaga, Keinosuke; Larry D. Hostetler. The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory[J].197521(1):32-40.
[200]M. Charikar, Greedy approximation algorithms for finding dense components in a graph [C]. In Proceeding of the 3rd International Workshop on Approximation Algorithms for Combinatorial Optimization (APPROX'00),2000, Springer-Verlag London, UK 84-95.
[201]Web spam challenge [EB/OL]. http://webspam.lip6.fr/wiki/pmwiki.php,2007.
[202]J. Newsome, E. Shi, D. Song, and A. Perrig, The sybil attack in sensor networks: analysis & defenses[C]. In Proceedings of the 3rd international symposium on Information processing in sensor networks (IPSN '04) 2004, ACM, New York. USA.259-268.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |