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

文献类型：期刊论文

作者Tang, Yongqiang1; Xie, Yuan3; Zhang, Wensheng1,2
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2023-08-01
卷号45期号:8页码:9357-9373
ISSN号0162-8828
关键词Affine subspace low-rank representation low-rank tensor multi-view learning subspace clustering
DOI10.1109/TPAMI.2023.3257407
通讯作者Xie, Yuan(yxie@cs.ecnu.edu.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要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.
WOS关键词CLASSIFICATION ; FACTORIZATION ; APPROXIMATION ; ALGORITHM
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
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:001022958600006
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/53936]  
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
3.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200050, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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