UM
Cauchy Matching Pursuit for Robust Sparse Representation and Classification
Wang Y.3; Zou C.3; Tang Y.Y.1; Li L.2
2018-11-26
Source PublicationProceedings - International Conference on Pattern Recognition
Volume2018-August
Pages694-698
AbstractVarious greedy algorithms have been developed for sparse signal recovery in recent years. However, most of them utilize the \ell-{2} norm based loss function and sensitive to non-Gaussian noises and outliers. This paper proposes a Cauchy matching pursuit (CauchyMP) algorithm for robust sparse representation and classification. By leveraging a Cauchy estimator based loss function, the proposed approach can robustly learn the sparse representation of noisy data corrupted by various severe noises. As a greedy algorithm, CauchyMP is also computationally efficient. We also develop a CauchyMP based classifier for robust classification with application to face recognition. The experiments on the datasets with gross corruptions demonstrate the efficacy and robustness of CauchyMP for learning robust sparse representation.
DOI10.1109/ICPR.2018.8546164
URLView the original
Language英語
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Hubei University
3.Chengdu University
Recommended Citation
GB/T 7714
Wang Y.,Zou C.,Tang Y.Y.,et al. Cauchy Matching Pursuit for Robust Sparse Representation and Classification[C],2018:694-698.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang Y.]'s Articles
[Zou C.]'s Articles
[Tang Y.Y.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang Y.]'s Articles
[Zou C.]'s Articles
[Tang Y.Y.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang Y.]'s Articles
[Zou C.]'s Articles
[Tang Y.Y.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.