Isomap算法在地震属性参数降维中的应用
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摘要
本文针对非线性高维地震属性参数降维的困难,引入了一种新的非线性降维方法Isomap,并将Isomap降维的结果与线性的MDS降维结果通过小波神经网络进行检验,从算法原理的角度讨论了Isomap算法在地震属性参数降维处理中的可适性.
It is difficult to find the intrinsic structure of high dimensional seismic attribute parameterdata.The new method named Isomap is applied and matched with MDS by WNN.It proves a good effect in the analysis of seismic attribute parameter data.
It is difficult to find the intrinsic structure of high dimensional seismic attribute parameterdata.The new method named Isomap is applied and matched with MDS by WNN.It proves a good effect in the analysis of seismic attribute parameter data.
引文
[1]JOHN ALDO LEE,AMAURY LENDASSE,MICHEN VERLEYSEN.Nonlinear projection with curvilinear distances[J].Isomap versus curvilinear distance analysis.Neurocomputing,2004,57(1):49-76.
[2]HONG TANG,TAO FANG,PENG-FEI SHI.Nonlinear discriminant mapping using the Laplacian of a graph.Pattern Recognition,2006,39:156-159.
[3]DAVID L.DONOHO;CARRIE GRIMES.Locally Linear Embedding Techniques for High-Dimensional Data.Proceedings of the National Academy of Sciences of the United States of America,2003,100(10):5591-559.
[4]SAM T.ROWEIS;LAWRENCE K.Saul.Nonlinear Dimensionality Reduction by Locall Linear Embedding.Science,New Series,2000,290:2323-2326.
[5]DIMITRIS K,AGRAFIOTIS;HUAFENG XU.A Self-Organizing Principle for Learning Nonlinear Manifolds.Proceedings of the National Academy of Sciences of the United States of America,2002,99:15869-15872.
[6]ZHANG ZHEN-YUE,ZHA HONG-YUAN.Principal Manifolds and Nonlinear Dimensinality Reduction via Tangent Space Alignment.Journal of Shanghai University(English Edition),2004,8(4):406-424.
[7]翁时锋,张长水,张学工.非线性降维在高维医学数据处理中的应用[J].清华大学学报,2004,44(4):485-488.
[8]袁远,季星来,孙之荣,等.Isomap在基因表达普数据聚类分析中的应用[J].清华大学学报,2004,44(9):1286-1289.
[9]刘春梅,戴汝为.综合集成研讨厅专家群体评估结果的可视化[J].模式识别与人工智能,2005,18(1):6-12.
[10]赵连伟,罗四维,赵艳敞,等.高维数据流形的低维嵌入及维数研究[J].软件学报,2005,16(8):1423-1430.
[11]侯越先,丁峥,何丕廉.基于自组织的鲁棒非线性维数约减算法[J].计算机研究与发展,2005,42(2):188-195.
[2]HONG TANG,TAO FANG,PENG-FEI SHI.Nonlinear discriminant mapping using the Laplacian of a graph.Pattern Recognition,2006,39:156-159.
[3]DAVID L.DONOHO;CARRIE GRIMES.Locally Linear Embedding Techniques for High-Dimensional Data.Proceedings of the National Academy of Sciences of the United States of America,2003,100(10):5591-559.
[4]SAM T.ROWEIS;LAWRENCE K.Saul.Nonlinear Dimensionality Reduction by Locall Linear Embedding.Science,New Series,2000,290:2323-2326.
[5]DIMITRIS K,AGRAFIOTIS;HUAFENG XU.A Self-Organizing Principle for Learning Nonlinear Manifolds.Proceedings of the National Academy of Sciences of the United States of America,2002,99:15869-15872.
[6]ZHANG ZHEN-YUE,ZHA HONG-YUAN.Principal Manifolds and Nonlinear Dimensinality Reduction via Tangent Space Alignment.Journal of Shanghai University(English Edition),2004,8(4):406-424.
[7]翁时锋,张长水,张学工.非线性降维在高维医学数据处理中的应用[J].清华大学学报,2004,44(4):485-488.
[8]袁远,季星来,孙之荣,等.Isomap在基因表达普数据聚类分析中的应用[J].清华大学学报,2004,44(9):1286-1289.
[9]刘春梅,戴汝为.综合集成研讨厅专家群体评估结果的可视化[J].模式识别与人工智能,2005,18(1):6-12.
[10]赵连伟,罗四维,赵艳敞,等.高维数据流形的低维嵌入及维数研究[J].软件学报,2005,16(8):1423-1430.
[11]侯越先,丁峥,何丕廉.基于自组织的鲁棒非线性维数约减算法[J].计算机研究与发展,2005,42(2):188-195.
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