CASIA OpenIR
Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning
Xie, Yuan1; Zhang, Wensheng1; Qu, Yanyun2; Dai, Longquan3; Tao, Dacheng4,5
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2020-02-01
Volume50Issue:2Pages:572-586
Corresponding AuthorQu, Yanyun(yyqu@xmu.edu.cn)
AbstractIn this paper, we address the multiview nonlinear subspace representation problem. Traditional multiview subspace learning methods assume that the heterogeneous features of the data usually lie within the union of multiple linear subspaces. However, instead of linear subspaces, the data feature actually resides in multiple nonlinear subspaces in many real-world applications, resulting in unsatisfactory clustering performance. To overcome this, we propose a hyper-Laplacian regularized multilinear multiview self-representation model, which is referred to as HLR-(MVS)-V-2, to jointly learn multiple views correlation and a local geometrical structure in a unified tensor space and view-specific self-representation feature spaces, respectively. In unified tensor space, a well-founded tensor low-rank regularization is adopted to impose on the self-representation coefficient tensor to ensure global consensus among different views. In view-specific feature space, hypergraph-induced hyper-Laplacian regularization is utilized to preserve the local geometrical structure embedded in a high-dimensional ambient space. An efficient algorithm is then derived to solve the optimization problem of the established model with theoretical convergence guarantee. Furthermore, the proposed model can be extended to semisupervised classification without introducing any additional parameters. An extensive experiment of our method is conducted on many challenging datasets, where a clear advance over state-of-the-art multiview clustering and multiview semisupervised classification approaches is achieved.
KeywordTensile stress Manifolds Encoding Laplace equations Semisupervised learning Correlation Optimization Manifold regularization multilinear multiview features nonlinear subspace clustering tensor singular-value decomposition (t-SVD)
DOI10.1109/TCYB.2018.2869789
WOS KeywordSPARSE ; CLASSIFICATION ; RECOGNITION ; FRAMEWORK ; ALGORITHM ; SCENE ; GRAPH
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61772524] ; National Natural Science Foundation of China[61772525] ; 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[30917011323] ; Australian Research Council[FT-130101457] ; Australian Research Council[DP-120103730] ; Australian Research Council[LP-150100671] ; National Natural Science Foundation of China[61772524] ; National Natural Science Foundation of China[61772525] ; 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[30917011323] ; Australian Research Council[FT-130101457] ; Australian Research Council[DP-120103730] ; Australian Research Council[LP-150100671]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Fundamental Research Funds for the Central Universities ; Australian Research Council
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000506849800015
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29494
Collection中国科学院自动化研究所
Corresponding AuthorQu, Yanyun
Affiliation1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
2.Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China
3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
4.Univ Sydney, UBTECH Sydney Artificial Intelligence Ctr, Darlington, NSW 2008, Australia
5.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, Darlington, NSW 2008, Australia
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xie, Yuan,Zhang, Wensheng,Qu, Yanyun,et al. Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(2):572-586.
APA Xie, Yuan,Zhang, Wensheng,Qu, Yanyun,Dai, Longquan,&Tao, Dacheng.(2020).Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning.IEEE TRANSACTIONS ON CYBERNETICS,50(2),572-586.
MLA Xie, Yuan,et al."Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning".IEEE TRANSACTIONS ON CYBERNETICS 50.2(2020):572-586.
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