Dynamic auto-weighted multi-view co-clustering

Abstract

To exploit the complementary information of multi-view data, many weighted multi-view clustering methods have been proposed and have demonstrated impressive performance. However, most of these methods learn the view weights by introducing additional parameters, which can not be easily obtained in practice. Moreover, they all simply apply the learned weights on the original feature representation of each view, which may deteriorate the clustering performance in the case of high-dimensional data with redundancy and noise. In this paper, we extend information bottleneck co-clustering into a multi-view framework and propose a novel dynamic auto-weighted multi-view co-clustering algorithm to learn a group of weights for views with no need for extra weight parameters. By defining the new concept of the discrimination-compression rate, we quantify the importance of each view by evaluating the discriminativeness of the compact features (i.e., feature-wise clusters) of the views. Unlike existing weighted methods that impose weights on the original feature representations of multiple views, we apply the learned weights on the discriminative ones, which can reduce the negative impact of noisy features in high-dimensional data. To solve the optimization problem, a new two-step sequential method is designed. Experimental results on several datasets show the advantages of the proposed algorithm. To our knowledge, this is the first work incorporating weighting scheme into multi-view co-clustering framework.