UM
Information-theoretic generalized orthogonal matching pursuit for robust pattern classification
Wang Y.3; Tang Y.Y.2; Zou C.3; Yang L.1
2017-11-27
Source Publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Volume2017-January
Pages501-506
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.
DOI10.1109/SMC.2017.8122655
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Guangxi University
2.Universidade de Macau
3.Chengdu University
Recommended Citation
GB/T 7714
Wang Y.,Tang Y.Y.,Zou C.,et al. Information-theoretic generalized orthogonal matching pursuit for robust pattern classification[C],2017:501-506.
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