Joint Intra-view and Inter-view Enhanced Tensor Low-rank Induced Affinity Graph Learning

Graph-based and tensor-based multi-view clustering have gained popularity in recent years due to their ability to explore the relationship between samples. However, there are still several shortcomings in the current multi-view graph clustering algorithms. (1) Most previous methods only focus on the inter-view correlation, while ignoring the intra-view correlation. (2) They usually use the Tensor Nuclear Norm (TNN) to approximate the rank of tensors. However, while it has the same penalty for different singular values, the model cannot approximate the true rank of tensors well. To solve these problems in a unified way, we propose a new tensor-based multi-view graph clustering method. Specifically, we introduce the Enhanced Tensor Rank (ETR) minimization of intra-view and inter-view in the process of learning the affinity graph of each view. Compared with 10 state-of-the-art methods on 8 real datasets, the experimental results demonstrate the superiority of our method. [Display omitted] •A new tensor-based multi-view graph clustering method is designed.•Enhanced Tensor Rank minimization (ETR) is incorporated to approximate the true rank of the tensor in the process of graph learning.•An iterative optimization algorithm is proposed to solve the proposed model.