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Kernel based weighted group sparse representation classifier
Xu B.1; Guo P.1; Chen C.L.P.2
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8008 LNCS
IssuePART 5
AbstractSparse 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. © 2013 Springer-Verlag.
KeywordGroup sparse representation image classification kernel method
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Document TypeConference paper
Affiliation1.Beijing Normal University
2.Universidade de Macau
Recommended Citation
GB/T 7714
Xu B.,Guo P.,Chen C.L.P.. Kernel based weighted group sparse representation classifier[C],2013:236-245.
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