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Modal-Regression-Based Structured Low-Rank Matrix Recovery for Multiview Learning
Xu, Jiamiao1; Wang, Fangzhao1; Peng, Qinmu1,4; You, Xinge1,4; Wang, Shuo1; Jing, Xiao Yuan2; Chen, C. L.Philip3,5,6
2021-03-01
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume32Issue:3Pages:1204-1216
Abstract

Low-rank Multiview Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL-based methods are incapable of handling well view discrepancy and discriminancy simultaneously, which, thus, leads to performance degradation when there is a large discrepancy among multiview data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose structured low-rank matrix recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of the structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address the data contaminated by the predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical, since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such a limitation, modal regression is elegantly incorporated into the framework of SLMR (termed MR-SLMR). Different from previous LMvSL-based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise, and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to optimize efficiently MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise.

KeywordBlock-diagonal Representation Learning Cross-view Classification Low-rank Representation Multiview Learning
DOI10.1109/TNNLS.2020.2980960
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000626332700021
Scopus ID2-s2.0-85102264755
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorPeng, Qinmu
Affiliation1.School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
2.State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, China
3.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, Macao
4.Shenzhen Research Institute, Huazhong University of Science and Technology, Shenzhen, 518000, China
5.Navigation College, Dalian Maritime University, Dalian, 116026, China
6.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China
Recommended Citation
GB/T 7714
Xu, Jiamiao,Wang, Fangzhao,Peng, Qinmu,et al. Modal-Regression-Based Structured Low-Rank Matrix Recovery for Multiview Learning[J]. IEEE Transactions on Neural Networks and Learning Systems,2021,32(3):1204-1216.
APA Xu, Jiamiao,Wang, Fangzhao,Peng, Qinmu,You, Xinge,Wang, Shuo,Jing, Xiao Yuan,&Chen, C. L.Philip.(2021).Modal-Regression-Based Structured Low-Rank Matrix Recovery for Multiview Learning.IEEE Transactions on Neural Networks and Learning Systems,32(3),1204-1216.
MLA Xu, Jiamiao,et al."Modal-Regression-Based Structured Low-Rank Matrix Recovery for Multiview Learning".IEEE Transactions on Neural Networks and Learning Systems 32.3(2021):1204-1216.
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