A Parameter-free Multi-view Information Bottleneck Clustering Method by Cross-view Weighting

With the fast-growing multi-modal/media data in the Big Data era, multi-view clustering (MVC) has attracted lots of attentions lately. Most MVCs focus on integrating and utilizing the complementary information among views by linear sum of the learned view weights and have shown great success in some fields. However, they fail to quantify how complementary the information across views actually utilized for benefiting final clustering. Additionally, most of them contain at least one parameter for regularization without prior knowledge, which puts pressure on the parameter-tuning and thus makes them impractical. In this paper, we propose a novel parameter-free multi-view information bottleneck (PMIB) clustering method to automatically identify and exploit useful complementary information among views, thus reducing the negative impact from the harmful views. Specifically, we first discover the informative view by measuring the relevant information preserved by the original data and the compact clusters with mutual information. Then, a new cross-view weight learning scheme is designed to learn how complementary between the informative view and remaining views. Finally, the quantitative correlations among views are fully exploited to improve the clustering performance without needing any additional parameters or prior knowledge. Experimental results on different kinds of multi-view datasets show the effectiveness of the proposed method.