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Kernel Based Weighted Group Sparse Representation Classifier

  • Bingxin Xu
  • Ping Guo
  • C. L. Philip Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)

Abstract

Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, SRC can not well classify the data when they are in the overlap feature space. In addition, SRC treats different samples equally and ignores the cooperation among samples belong to the same class. In this paper, a kernel based weighted group sparse classifier (KWGSC) is proposed. Kernel trick is not only used for mapping the original feature space into a high dimensional feature space, but also as a measure to select members of each group. The weight reflects the importance degree of training samples in different group. Substantial experiments on benchmark databases have been conducted to investigate the performance of proposed method in image classification. The experimental results demonstrate that the proposed KWGSC approach has a higher classification accuracy than that of SRC and other modified sparse representation classification.

Keywords

Group sparse representation kernel method image classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bingxin Xu
    • 1
  • Ping Guo
    • 1
  • C. L. Philip Chen
    • 2
  1. 1.Image Processing and Pattern Recognition LaboratoryBeijing Normal UniversityBeijingChina
  2. 2.Faculty of Science and TechnologyUniversity of MacauMacauChina

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