Robust Kernelized Multiview Self-Representation for Subspace Clustering

doi:10.1109/TNNLS.2020.2979685

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Robust Kernelized Multiview Self-Representation for Subspace Clustering
	Xie, Yuan1 ; Liu, Jinyan 2; Qu, Yanyun 2; Tao, Dacheng 3; Zhang, Wensheng4 ; Dai, Longquan5 ; Ma, Lizhuang 1
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021-02-01
卷号	32 期号:2 页码:868-881
通讯作者	Qu, Yanyun(yyqu@xmu.edu.cn)
摘要	In this article, we propose a multiview self-representation model for nonlinear subspaces clustering. By assuming that the heterogeneous features lie within the union of multiple linear subspaces, the recent multiview subspace learning methods aim to capture the complementary and consensus from multiple views to boost the performance. However, in real-world applications, data feature usually resides in multiple nonlinear subspaces, leading to undesirable results. To this end, we propose a kernelized version of tensor-based multiview subspace clustering, which is referred to as Kt-SVD-MSC, to jointly learn self-representation coefficients in mapped high-dimensional spaces and multiple views correlation in unified tensor space. In view-specific feature space, a kernel-induced mapping is introduced for each view to ensure the separability of self-representation coefficients. In unified tensor space, a new kind of tensor low-rank regularizer is employed on the rotated self-representation coefficient tensor to preserve the global consistency across different views. We also derive an algorithm to efficiently solve the optimization problem with all the subproblems having closed-form solutions. Furthermore, by incorporating the nonnegative and sparsity constraints, the proposed method can be easily extended to a useful variant, meaning that several useful variants can be easily constructed in a similar way. Extensive experiments of the proposed method are tested on eight challenging data sets, in which a significant (even a breakthrough) advance over state-of-the-art multiview clustering is achieved.
关键词	Tensile stress Kernel Manifolds Optimization Learning systems Correlation Data models Kernelization multiview subspace learning nonlinear subspace clustering tensor singular value decomposition (t-SVD)
DOI	10.1109/TNNLS.2020.2979685
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61772524] ; National Natural Science Foundation of China[61876161] ; National Natural Science Foundation of China[61701235] ; National Natural Science Foundation of China[61373077] ; National Natural Science Foundation of China[61602482] ; Beijing Municipal Natural Science Foundation[4182067] ; Fundamental Research Funds for the Central Universities ; Shanghai Key Laboratory of Trustworthy Computing ; Open Projects Program of the National Laboratory of Pattern Recognition of China[201900020]
项目资助者	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Fundamental Research Funds for the Central Universities ; Shanghai Key Laboratory of Trustworthy Computing ; Open Projects Program of the National Laboratory of Pattern Recognition of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000616310400032
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：55[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/173211/43279
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Qu, Yanyun
作者单位	1.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200062, Peoples R China 2.Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China 3.Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 5.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
推荐引用方式 GB/T 7714	Xie, Yuan,Liu, Jinyan,Qu, Yanyun,et al. Robust Kernelized Multiview Self-Representation for Subspace Clustering[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(2):868-881.
APA	Xie, Yuan.,Liu, Jinyan.,Qu, Yanyun.,Tao, Dacheng.,Zhang, Wensheng.,...&Ma, Lizhuang.(2021).Robust Kernelized Multiview Self-Representation for Subspace Clustering.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(2),868-881.
MLA	Xie, Yuan,et al."Robust Kernelized Multiview Self-Representation for Subspace Clustering".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.2(2021):868-881.