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Robust deep multi-view subspace clustering networks with a correntropy-induced metric

文献类型：期刊论文

作者Si, Xiaomeng2; Yin, Qiyue1; Zhao, Xiaojie2; Yao, Li2
刊名APPLIED INTELLIGENCE
出版日期2022-03-30
页码17
ISSN号0924-669X
关键词Subspace clustering Multi-view learning Deep clustering Consistency Diversity
DOI10.1007/s10489-022-03209-9
通讯作者Yao, Li(yaoli@bnu.edu.cn)
英文摘要Since multi-view subspace clustering combines the advantages of deep learning to capture the nonlinear nature of data, deep multi-view subspace clustering methods have demonstrated superior ability to shallow multi-view subspace clustering methods. Most existing methods assume that sample reconstruction errors incurred by noise conform to the prior distribution of the corresponding norm, allowing for simplification of the problem and focus on designing specific regularization on self-representation matrices to exploit consistent and diverse information among different views. However, the noise distributions in different views are always very complex, and in practice the noise distributions do not necessarily conform to this hypothesis. Furthermore, the commonly used diversity regularization based on value-awareness to enhance diversity among different view representations is not sufficiently accurate. To alleviate the above deficiencies, we propose novel robust deep multi-view subspace clustering networks with a correntropy-induced metric (RDMSCNet). (1) A correntropy-induced metric (CIM) is utilized to flexibly handle various complex noise distributions in a data-driven manner to improve the robustness of the model. (2) A position-aware diversity regularization based on the exclusivity definition is employed to enforce the diversity of the different view representations for modelling the consistency and diversity simultaneously. Extensive experiments show that RDMSCNet can deliver enhanced performance over state-of-the-art approaches.
WOS关键词MOTION SEGMENTATION
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
语种英语
出版者SPRINGER
WOS记录号WOS:000774722900001
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/173211/48173]  
专题智能系统与工程
通讯作者Yao, Li
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Beijing Normal Univ, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Si, Xiaomeng,Yin, Qiyue,Zhao, Xiaojie,et al. Robust deep multi-view subspace clustering networks with a correntropy-induced metric[J]. APPLIED INTELLIGENCE,2022:17.
APA Si, Xiaomeng,Yin, Qiyue,Zhao, Xiaojie,&Yao, Li.(2022).Robust deep multi-view subspace clustering networks with a correntropy-induced metric.APPLIED INTELLIGENCE,17.
MLA Si, Xiaomeng,et al."Robust deep multi-view subspace clustering networks with a correntropy-induced metric".APPLIED INTELLIGENCE (2022):17.

入库方式： OAI收割

来源：自动化研究所

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