Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data

In this paper, we propose a novel incomplete multi-view clustering method, in which a tensor nuclear norm regularizer elegantly diffuses the information of multi-view block-diagonal structure across different views. By exploring the membership between observed and missing samples and that between missing ones in each incomplete view with the guidance of the high-order view consistency, a global block-diagonal structure is well preserved in multiple spectral embedding matrices. Meanwhile, a consensus representation with strong separability is obtained for clustering. An iterative algorithm based on Augmented Lagrange Multiplier (ALM) is designed to solve the resultant model. Experimental results on six benchmark datasets indicate the superiority of the proposed method.



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