Multi-view subspace clustering with intactness-aware similarity
Wang, Xiaobo1; Lei, Zhen2,3; Guo, Xiaojie4; Zhang, Changqing4; Shi, Hailin1; Li, Stan Z.2,3,5
Corresponding AuthorLei, Zhen(
AbstractMulti-view subspace clustering, which aims to partition a set of multi-source data into their underlying groups, has recently attracted intensive attention from the communities of pattern recognition and data mining. This paper proposes a novel multi-view subspace clustering model that attempts to form an informative intactness-aware similarity based on the intact space learning technique. More specifically, we learn an intact space by integrating encoded complementary information. An informative similarity matrix is simultaneously constructed, which enforces the constructed similarity to have maximum dependence with its latent intact points by adopting the Hilbert-Schmidt Independence Criterion (HSIC). A new explanation on the advantages of such intactness-aware similarity has been provided (i.e., the similarity is learned according to the local connectivity). To effectively and efficiently seek the optimal solution of the associated problem, a new ADMM based algorithm is designed. Moreover, to show the merit of the proposed joint optimization, we also conduct the clustering in two separated steps. Extensive experimental results on six benchmark datasets are provided to reveal the effectiveness of the proposed algorithm and its superior performance over other state-of-the-art alternatives. (C) 2018 Published by Elsevier Ltd.
KeywordIntact space Intactness-aware similarity Multi-view subspace clustering
Indexed BySCI
Funding ProjectNational Key Research and Development Plan[2016YFC0801002] ; Chinese National Natural Science Foundation[61876178] ; Chinese National Natural Science Foundation[61473291] ; Chinese National Natural Science Foundation[61572501] ; Chinese National Natural Science Foundation[61502491] ; Chinese National Natural Science Foundation[61572536] ; Science and Technology Development Fund of Macau[151/2017/A] ; Science and Technology Development Fund of Macau[152/2017/A] ; AuthenMetric RD Funds
Funding OrganizationNational Key Research and Development Plan ; Chinese National Natural Science Foundation ; Science and Technology Development Fund of Macau ; AuthenMetric RD Funds
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000457666900005
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorLei, Zhen
Affiliation1.JD AI Res, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, CBSR, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China
4.Tianjin Univ, Tianjin, Peoples R China
5.Macau Univ Sci & Technol, Fac Informat Technol, Taipa, Macao, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Xiaobo,Lei, Zhen,Guo, Xiaojie,et al. Multi-view subspace clustering with intactness-aware similarity[J]. PATTERN RECOGNITION,2019,88:50-63.
APA Wang, Xiaobo,Lei, Zhen,Guo, Xiaojie,Zhang, Changqing,Shi, Hailin,&Li, Stan Z..(2019).Multi-view subspace clustering with intactness-aware similarity.PATTERN RECOGNITION,88,50-63.
MLA Wang, Xiaobo,et al."Multi-view subspace clustering with intactness-aware similarity".PATTERN RECOGNITION 88(2019):50-63.
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