Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor

doi:10.1109/TPAMI.2023.3257407

	Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor
	Tang, Yongqiang1 ; Xie, Yuan2 ; Zhang, Wensheng1,3
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
	2023-08-01
卷号	45 期号:8 页码:9357-9373
通讯作者	Xie, Yuan(yxie@cs.ecnu.edu.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
摘要	Low-rank self-representation based subspace learning has confirmed its great effectiveness in a broad range of applications. Nevertheless, existing studies mainly focus on exploring the global linear subspace structure, and cannot commendably handle the case where the samples approximately (i.e., the samples contain data errors) lie in several more general affine subspaces. To overcome this drawback, in this paper, we innovatively propose to introduce affine and nonnegative constraints into low-rank self-representation learning. While simple enough, we provide their underlying theoretical insight from a geometric perspective. The union of two constraints geometrically restricts each sample to be expressed as a convex combination of other samples in the same subspace. In this way, when exploring the global affine subspace structure, we can also consider the specific local distribution of data in each subspace. To comprehensively demonstrate the benefits of introducing two constraints, we instantiate three low-rank self-representation methods ranging from single-view low-rank matrix learning to multi-view low-rank tensor learning. We carefully design the solution algorithms to efficiently optimize the proposed three approaches. Extensive experiments are conducted on three typical tasks, including single-view subspace clustering, multi-view subspace clustering, and multi-view semi-supervised classification. The notably superior experimental results powerfully verify the effectiveness of our proposals.
关键词	Affine subspace low-rank representation low-rank tensor multi-view learning subspace clustering
DOI	10.1109/TPAMI.2023.3257407
关键词[WOS]	CLASSIFICATION ; FACTORIZATION ; APPROXIMATION ; ALGORITHM
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021ZD0111000] ; National Natural Science Foundation of China[62106266] ; National Natural Science Foundation of China[U22B2048] ; National Natural Science Foundation of China[62222602] ; National Natural Science Foundation of China[62176092] ; Shanghai Science and Technology Commission[21511100700] ; Natural Science Foundation of Shanghai[20ZR1417700] ; CAAI-Huawei MindSporeOpen Fund
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Shanghai Science and Technology Commission ; Natural Science Foundation of Shanghai ; CAAI-Huawei MindSporeOpen Fund
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001022958600006
出版者	IEEE COMPUTER SOC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53937
专题	多模态人工智能系统全国重点实验室
通讯作者	Xie, Yuan; Zhang, Wensheng
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China 2.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200050, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Tang, Yongqiang,Xie, Yuan,Zhang, Wensheng. Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(8):9357-9373.
APA	Tang, Yongqiang,Xie, Yuan,&Zhang, Wensheng.(2023).Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(8),9357-9373.
MLA	Tang, Yongqiang,et al."Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.8(2023):9357-9373.