Multi-view clustering via clusterwise weights learning

It is known that the performance of multi-view clustering could be improved by assigning weights to the views, since different views play different roles in the final clustering results. Nevertheless, we observe that weights could be further refined, since in reality different clusters also have different impacts on finding the correct results. We propose a multi-view clustering algorithm with clusterwise weights (MCW), which assigns a weight on each cluster within each view. The objective function of MCW consists of three parts: (1) intra-view clustering: clustering each view by using non-negative matrix factorization; (2) inter-view relationship learning: learning the consensus clustering results by a weighted combination of each view; (3) clusterwise weight learning: learning the weight of a cluster by making the weight be proportional to the average distance between the cluster and other clusters. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results on several benchmark datasets demonstrate the superiority of the proposed algorithm over existing multi-view clustering methods.