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

doi:10.1007/s10489-022-03209-9

	Robust deep multi-view subspace clustering networks with a correntropy-induced metric
	Si, Xiaomeng 1; Yin, Qiyue2 ; Zhao, Xiaojie 1; Yao, Li 1
发表期刊	APPLIED INTELLIGENCE
ISSN	0924-669X
	2022-03-30
页码	17
通讯作者	Yao, Li(yaoli@bnu.edu.cn)
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.
关键词	Subspace clustering Multi-view learning Deep clustering Consistency Diversity
DOI	10.1007/s10489-022-03209-9
关键词[WOS]	MOTION SEGMENTATION
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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000774722900001
出版者	SPRINGER
引用统计	被引频次：3[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/173211/48174
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. 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.