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Generalized Latent Multi-View Subspace Clustering

文献类型：期刊论文

作者Zhang, Changqing6; Fu, Huazhu2; Hu, Qinghua6; Cao, Xiaochun5; Xie, Yuan3; Tao, Dacheng4; Xu, Dong1
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2020
卷号42期号:1页码:86-99
ISSN号0162-8828
关键词Clustering 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
通讯作者Zhang, Changqing(zhangchangqing@tju.edu.cn) ; Hu, Qinghua(huqinghua@tju.edu.cn)
英文摘要Subspace 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.
WOS关键词ALGORITHM ; SPARSE
资助项目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]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:000502294300007
资助机构National Natural Science Foundation of China ; NIH ; Australian Research Council Projects
源URL[http://ir.ia.ac.cn/handle/173211/29459]  
专题自动化研究所_精密感知与控制研究中心
通讯作者Zhang, Changqing; Hu, Qinghua
作者单位1.Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia
2.Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates
3.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
4.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, UBTECH Sydney Artificial Intelligence Ctr, 6 Cleveland St, Darlington, NSW 2008, Australia
5.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China
6.Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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