Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization

When multiple views of data are available for a set of subjects, co-clustering aims to identify subject clusters that agree across the different views. We explore the problem of co-clustering when the underlying clusters exist in different subspaces of each view. We propose a proximal alternating linearized minimization algorithm that simultaneously decomposes multiple data matrices into sparse row and columns vectors. This approach is able to group subjects consistently across the views and simultaneously identify the subset of features in each view that are associated with the clusters. The proposed algorithm can globally converge to a critical point of the problem. A simulation study validates that the proposed algorithm can identify the hypothesized clusters and their associated features. Comparison with several latest multi-view co-clustering methods on benchmark datasets demonstrates the superior performance of the proposed approach.