Sparse Co-Clustering via Multi-view Matrix Decomposition

Jinbo Bi, Jiangwen Sun, and Tingyang Xu

Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA



Abstract.

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 coclustering when the underlying clusters exist in different subspaces of each view. We propose a multi-view singular value decomposition approach that simultaneously decomposes multiple data matrices into sparse singular vectors. This approach is able to group subjects consistently across the views and simultaneously identify the subset of features in each view that specifies the clusters. Extension to multi-view eigen-decomposition based on the same idea is also discussed. 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.




Contact Jinbo Bi (jinbo@engr.uconn.edu) for information about this page.