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Generalized Latent Multi-View Subspace Clustering
Zhang, Changqing1; Fu, Huazhu2; Hu, Qinghua1; Cao, Xiaochun3; Xie, Yuan4; Tao, Dacheng5; Xu, Dong6
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2020
Volume42Issue:1Pages:86-99
Corresponding AuthorZhang, Changqing(zhangchangqing@tju.edu.cn) ; Hu, Qinghua(huqinghua@tju.edu.cn)
AbstractSubspace clustering is an effective method that has been successfully applied to many applications. Here, we propose a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC). Unlike most existing single-view subspace clustering methods, which directly reconstruct data points using original features, our method explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation. Using the complementarity of multiple views, the latent representation depicts data more comprehensively than each individual view, accordingly making subspace representation more accurate and robust. We proposed two LMSC formulations: linear LMSC (lLMSC), based on linear correlations between latent representation and each view, and generalized LMSC (gLMSC), based on neural networks to handle general relationships. The proposed method can be efficiently optimized under the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework. Extensive experiments on diverse datasets demonstrate the effectiveness of the proposed method.
KeywordClustering methods Correlation Electronic mail Neural networks Task analysis Clustering algorithms Minimization Multi-view clustering subspace clustering latent representation neural networks
DOI10.1109/TPAMI.2018.2877660
WOS KeywordALGORITHM ; SPARSE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61602337] ; National Natural Science Foundation of China[61732011] ; National Natural Science Foundation of China[61432011] ; National Natural Science Foundation of China[U1435212] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61733007] ; National Natural Science Foundation of China[61602345] ; NIH[CA206100] ; NIH[MH100217] ; Australian Research Council Projects[FL-170100117] ; Australian Research Council Projects[DP-180103424] ; National Natural Science Foundation of China[61602337] ; National Natural Science Foundation of China[61732011] ; National Natural Science Foundation of China[61432011] ; National Natural Science Foundation of China[U1435212] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61733007] ; National Natural Science Foundation of China[61602345] ; NIH[CA206100] ; NIH[MH100217] ; Australian Research Council Projects[FL-170100117] ; Australian Research Council Projects[DP-180103424]
Funding OrganizationNational Natural Science Foundation of China ; NIH ; Australian Research Council Projects
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000502294300007
PublisherIEEE COMPUTER SOC
Citation statistics
Cited Times:168[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29460
Collection精密感知与控制研究中心
Corresponding AuthorZhang, Changqing; Hu, Qinghua
Affiliation1.Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China
2.Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates
3.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China
4.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
5.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, UBTECH Sydney Artificial Intelligence Ctr, 6 Cleveland St, Darlington, NSW 2008, Australia
6.Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia
Recommended Citation
GB/T 7714
Zhang, Changqing,Fu, Huazhu,Hu, Qinghua,et al. Generalized Latent Multi-View Subspace Clustering[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,42(1):86-99.
APA Zhang, Changqing.,Fu, Huazhu.,Hu, Qinghua.,Cao, Xiaochun.,Xie, Yuan.,...&Xu, Dong.(2020).Generalized Latent Multi-View Subspace Clustering.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,42(1),86-99.
MLA Zhang, Changqing,et al."Generalized Latent Multi-View Subspace Clustering".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 42.1(2020):86-99.
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