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Robust Kernelized Multiview Self-Representation for Subspace Clustering

文献类型：期刊论文

作者Xie, Yuan2; Liu, Jinyan1; Qu, Yanyun1; Tao, Dacheng3; Zhang, Wensheng4; Dai, Longquan5; Ma, Lizhuang2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-02-01
卷号32期号:2页码:868-881
ISSN号2162-237X
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)
DOI10.1109/TNNLS.2020.2979685
通讯作者Qu, Yanyun(yyqu@xmu.edu.cn)
英文摘要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.
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]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000616310400032
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/43278]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Qu, Yanyun
作者单位1.Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China
2.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200062, 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.

入库方式： OAI收割

来源：自动化研究所

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