Adaptive Tensor Rank Approximation for Multi-View Subspace Clustering
作者机构：College of Mathematics and Statistics Shenzhen University School of Electronic and Communication EngineeringShenzhen Polytechnic National Center for Applied Mathematics Shenzhen (NCAMS)
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2023年第32卷第4期
页 面：840-853页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 07[理学] 070104[理学-应用数学] 0701[理学-数学]
基 金：supported by the National Natural Science Foundation of China (61872429, 62272313, 12101415, 62202018) the Project of Educational Commission of Guangdong Province (2022KTSCX106)
主 题：Diversity Multi-view subspace clustering Tensor adaptive log-determinant View-specific sparsity Self-representation
摘 要：Multi-view subspace clustering under a tensor framework remains a challenging problem, which can be potentially applied to image classification, impainting, denoising, etc. There are some existing tensor-based multi-view subspace clustering models mainly making use of the consistency in different views through tensor nuclear norm(TNN). The diversity which means the intrinsic difference in individual view is always ignored. In this paper, a new tensorial multi-view subspace clustering model is proposed, which jointly exploits both the consistency and diversity in each view. The view representation is decomposed into view-consistent part(low-rank part) and view-specific part(diverse part). A tensor adaptive logdeterminant regularization(TALR) is imposed on the low-rank part to better relax the tensor multi-rank, and a view-specific sparsity regularization is applied on the diverse part to ensure connectedness property. Although the TALR minimization is not convex, it has a closedform analytical solution and its convergency is validated mathematically. Extensive evaluations on six widely used clustering datasets are executed and our model is demonstrated to have the superior performance.
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