UM
Information-Theoretic Generalized Orthogonal Matching Pursuit for Robust Pattern Classification
Wang, Yulong; Tang, Yuan Yan; Zou, Cuiming; Yang, Lina; IEEE
2017
Conference Name2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Pages501-506
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractOwing to its simplicity and efficacy, orthogonal matching pursuit (OMP) has been a popular sparse representation method for compressed sensing and pattern classification. As a recent extension of OMP, generalized OMP (GOMP) improves the efficiency of OMP by identifying multiple atoms each iteration. Nonetheless, GOMP utilizes the mean square error (MSE) criterion as the loss function, which has been proven to rely on the Gaussianity assumption of the noise distribution and sensitive to non-Gaussian noise. In this paper, we propose a robust sparse representation method, called information-theoretic generalized OMP (ITGOMP), to reduce the limitation of GOMP. The key idea is to minimize the correntropy based information-theoretic loss function, which is independent of the noise distribution. We also devise a half-quadratic based algorithm to tackle the optimization problem. Finally, an ITGOMP based classifier is developed for robust pattern classification. The experiments on public real-world databases verify the effectiveness and robustness of the proposed method for classification.
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000427598700088
The Source to ArticleWOS
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Wang, Yulong,Tang, Yuan Yan,Zou, Cuiming,et al. Information-Theoretic Generalized Orthogonal Matching Pursuit for Robust Pattern Classification[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:501-506.
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