Multi-view subspace clustering with intactness-aware similarity

doi:10.1016/j.patcog.2018.09.009

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Multi-view subspace clustering with intactness-aware similarity
	Wang, Xiaobo1 ; Lei, Zhen2,3 ; Guo, Xiaojie 4; Zhang, Changqing 4; Shi, Hailin1 ; Li, Stan Z.2,3,5
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2019-04-01
卷号	88 页码:50-63
通讯作者	Lei, Zhen(zlei@nlpr.ia.ac.cn)
摘要	Multi-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.
关键词	Intact space Intactness-aware similarity Multi-view subspace clustering
DOI	10.1016/j.patcog.2018.09.009
关键词[WOS]	ALGORITHM ; FUSION
收录类别	SCI
语种	英语
资助项目	National 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 ; National 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
项目资助者	National Key Research and Development Plan ; Chinese National Natural Science Foundation ; Science and Technology Development Fund of Macau ; AuthenMetric RD Funds
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000457666900005
出版者	ELSEVIER SCI LTD
七大方向——子方向分类	模式识别基础
引用统计	被引频次：114[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25270
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Lei, Zhen
作者单位	1.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
通讯作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
推荐引用方式 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.