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Weighted sparse coding regularized nonconvex matrix regression for robust face recognition
Zhang, Hengmin1; Yang, Jian1; Xie, Jianchun1; Qian, Jianjun1; Zhang, Bob2
2017-07
Source PublicationINFORMATION SCIENCES
ISSN0020-0255
Volume394–395Pages:1-17
Abstract

Most existing regression based classification methods for robust face recognition usually characterize the representation error using L-1-norm or Frobenius-norm for the pixel-level noise or nuclear norm for the image-level noise, and code the coefficients vector by l(1-) norm or l(2)-norm. To our best knowledge, nuclear norm can be used to describe the low rank structural information but may lead to the suboptimal solution, while l(1)-norm or l(2)-norm can promote the sparsity or cooperativity but may neglect the prior information (e.g., the locality and similarity relationship) among data. To solve these drawbacks, we propose two weighted sparse coding regularized nonconvex matrix regression models including weighted parse coding regularized matrix gamma-norm based matrix regression (WS gamma MR) for the structural noise and weighted parse coding regularized matrix gamma-norm plus minimax concave plus (MCP) function based matrix regression (WS gamma(MR)-R-2) for the mixed noise (e.g, structural noise plus sparse noise). The MCP induced nonconvex function can overcome the imbalanced penalization of different singular values and entries of the error image matrix, and the weighted sparse coding can consider the prior information by borrowing a novel distance metric. The variants of inexact augmented Lagrange multiplier (iALM) algorithm including nonconvex iALM (NCiALM) and majorization-minimization iALM (MMiALM) are developed to solve the proposed models, respectively. The matrix gamma-norm based classifier is devised for classification. Finally, experiments on four popular face image databases can validate the superiority of our methods compared with the-state-of-the-art regression methods.

KeywordNonconvex Matrix Regression Weighted Sparse Coding Inexact Augmented Lagrange Multiplier Method Face Recognition
DOI10.1016/j.ins.2017.02.020
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000396973000001
PublisherELSEVIER SCIENCE INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:29   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorYang, Jian
Affiliation1.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, PR China
2.Department of Computer and Information Science, University of Macau, Macau, PR China
Recommended Citation
GB/T 7714
Zhang, Hengmin,Yang, Jian,Xie, Jianchun,et al. Weighted sparse coding regularized nonconvex matrix regression for robust face recognition[J]. INFORMATION SCIENCES,2017,394–395:1-17.
APA Zhang, Hengmin,Yang, Jian,Xie, Jianchun,Qian, Jianjun,&Zhang, Bob.(2017).Weighted sparse coding regularized nonconvex matrix regression for robust face recognition.INFORMATION SCIENCES,394–395,1-17.
MLA Zhang, Hengmin,et al."Weighted sparse coding regularized nonconvex matrix regression for robust face recognition".INFORMATION SCIENCES 394–395(2017):1-17.
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