Nonconvex multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix*This research was supported by the National Natural Science Foundations of China (12071159, U1811464) and the NSF-DMS 1854638 of the United States. (1st October 2022)

• Record Type: Journal Article
• Authors: Li, Minghui; Li, Wen; Xiao, Mingqing
• Is Part Of: Inverse problems. Volume 38:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: multi-view subspace clustering -- hyper-Laplacian -- low-rank tensor -- tensor nuclear norm -- tensor singular value; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/ac8ac5
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: Multi-view subspace clustering, which aims to partition a dataset into its relevant subspaces based on their multi-view features, has been widely applied to identify various characteristics of datasets. The typical model of multi-view subspace clustering in literature often makes use of the nuclear norm to seek the underlying low-rank representation. However, due to the sum property of the singular values defined by tensor nuclear norm, the existing multi-view subspace clustering does not well handle the noise and the illumination variations embedded in multi-view data. To address and improve the robustness and clustering performance, we propose a new nonconvex multi-view subspace clustering model via tensor minimax concave penalty (MCP) approximation associated with rank minimization (NMSC-MCP), which can simultaneously construct the low-rank representation tensor and affinity matrix in a unified framework. Specifically, the nonconvex MCP approximation rank function is adopted to as a tighter tensor rank approximation to discriminate the dimension of features so that better accuracy can be achieved. In addition, we also address the local structure by including both hyper-Laplacian regularization and auto-weighting scheme into the objective function to promote the clustering performance. A corresponding iterative algorithm is then developed to solve the proposed model and the constructed iterative sequence generated by the proposed algorithm is shown to converge to the desirable KKT critical point. Extensive experiments on benchmark datasets have demonstrate the highly desirable effectiveness of our proposed method.