Semi-supervised classification based on subspace sparse representation

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• 作者：Guoxian Yu (1) (2)
  Guoji Zhang (3)
  Zili Zhang (4)
  Zhiwen Yu (2)
  Lin Deng (5)
  
  1. College of Computer and Information Science ; Southwest University ; Chongqing ; 400715 ; China
  2. School of Computer Science and Engineering ; South China University of Technology ; Guangzhou ; 510006 ; China
  3. School of Sciences ; South China University of Technology ; Guangzhou ; 510640 ; China
  4. School of Information Technology ; Deakin University ; Geelong ; VIC ; 3220 ; Australia
  5. Department of Computer Science ; George Mason University ; Fairfax ; VA ; 22030 ; USA
  
• 关键词：Semi ; supervised classification ; High ; dimensional data ; Graph construction ; Subspaces sparse representation
• 刊名：Knowledge and Information Systems
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：43
• 期：1
• 页码：81-101
• 全文大小：777 KB
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• 刊物类别：Computer Science
• 刊物主题：Information Systems and Communication Service
  Business Information Systems
  
• 出版者：Springer London
• ISSN：0219-3116

文摘

Graph plays an important role in graph-based semi-supervised classification. However, due to noisy and redundant features in high-dimensional data, it is not a trivial job to construct a well-structured graph on high-dimensional samples. In this paper, we take advantage of sparse representation in random subspaces for graph construction and propose a method called Semi-Supervised Classification based on Subspace Sparse Representation, SSC-SSR in short. SSC-SSR first generates several random subspaces from the original space and then seeks sparse representation coefficients in these subspaces. Next, it trains semi-supervised linear classifiers on graphs that are constructed by these coefficients. Finally, it combines these classifiers into an ensemble classifier by minimizing a linear regression problem. Unlike traditional graph-based semi-supervised classification methods, the graphs of SSC-SSR are data-driven instead of man-made in advance. Empirical study on face images classification tasks demonstrates that SSC-SSR not only has superior recognition performance with respect to competitive methods, but also has wide ranges of effective input parameters.