Consistent and diverse multi-View subspace clustering with structure constraint

doi:10.1016/j.patcog.2021.108196

	Consistent and diverse multi-View subspace clustering with structure constraint
	Si, Xiaomeng 1; Yin, Qiyue2 ; Zhao, Xiaojie 1; Yao, Li 1
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2022
卷号	121 页码:15
通讯作者	Yao, Li(yaoli@bnu.edu.cn)
摘要	Multi-view subspace clustering algorithms have recently been developed to process multi-view dataset clustering by accurately depicting the essential characteristics of multi-view data. Most existing methods focus on conduct self-representation property using a consistent representation and a set of specific representations with well-designed regularization to learn the common and specific knowledge among different views. However, specific representations only contain the unique information of each individual view, which limits their ability to fully excavate the diversity of multi-view data to enhance the complementarity among different views. Moreover, when conducting multi-view subspace clustering, the learned subspace self-representation and clustering are sequential and independent, which lacks consideration of the interaction between representation learning and the final clustering calculation. In this paper, a novel method termed consistent and diverse multi-view subspace clustering with structure constraint (CDMSC2) is proposed to overcome the above-described deficiencies. (1) An exclusivity constraint term is employed to enhance the diversity of specific representations among different views for modeling consistency and diversity in a unified framework. (2) A clustering structure constraint is imposed on the subspace self-representation by factorizing the learned subspace self-representation into the cluster centroids and the cluster assignments with the goal of obtaining a clustering-oriented subspace self-representation. In addition, we carefully designed an efficient optimization algorithm to solve the objective function through relaxation and alternating minimization. Extensive experiments on five benchmark datasets in terms of six evaluation metrics demonstrate that our method outperforms the state-of-the-art methods. (C) 2021 Elsevier Ltd. All rights reserved.
关键词	Subspace self-representation Multi-view clustering Consistency Diversity Clustering structure
DOI	10.1016/j.patcog.2021.108196
关键词[WOS]	ALGORITHM
收录类别	SCI
语种	英语
资助项目	Key Program of National Natural Science Foundation of China[61731003] ; Funds for National Natural Science Foundation of China[61871040]
项目资助者	Key Program of National Natural Science Foundation of China ; Funds for National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000701148300003
出版者	ELSEVIER SCI LTD
引用统计	被引频次：41[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/173211/45742
专题	复杂系统认知与决策实验室_智能系统与工程
通讯作者	Yao, Li
作者单位	1.Beijing Normal Univ, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Si, Xiaomeng,Yin, Qiyue,Zhao, Xiaojie,et al. Consistent and diverse multi-View subspace clustering with structure constraint[J]. PATTERN RECOGNITION,2022,121:15.
APA	Si, Xiaomeng,Yin, Qiyue,Zhao, Xiaojie,&Yao, Li.(2022).Consistent and diverse multi-View subspace clustering with structure constraint.PATTERN RECOGNITION,121,15.
MLA	Si, Xiaomeng,et al."Consistent and diverse multi-View subspace clustering with structure constraint".PATTERN RECOGNITION 121(2022):15.