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Consistent and diverse multi-View subspace clustering with structure constraint

文献类型：期刊论文

作者Si, Xiaomeng1; Yin, Qiyue2; Zhao, Xiaojie1; Yao, Li1
刊名PATTERN RECOGNITION
出版日期2022
卷号121页码:15
ISSN号0031-3203
关键词Subspace self-representation Multi-view clustering Consistency Diversity Clustering structure
DOI10.1016/j.patcog.2021.108196
通讯作者Yao, Li(yaoli@bnu.edu.cn)
英文摘要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.
WOS关键词ALGORITHM
资助项目Key Program of National Natural Science Foundation of China[61731003] ; Funds for National Natural Science Foundation of China[61871040]
WOS研究方向Computer Science ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000701148300003
资助机构Key Program of National Natural Science Foundation of China ; Funds for National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/45741]  
专题智能系统与工程
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.

入库方式： OAI收割

来源：自动化研究所

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