Mixture Self-paced Learning for Multi-view K-means Clustering

In our daily life, there are more and more data characterized by multiple features. In multi-view setting, the clusters estimated using single view have some limitations, and the quality of single view clustering can be improved by means of multi-view clustering. Self-paced learning simulates human learning process which can gradually combine information of views into clustering task from easy to complex. In this paper, we first propose a new mixture self-paced learning regularizer. To recap the effectiveness of regularizer, we combine it with robust multi-view k-means clustering and propose a new self-paced learning based multi-view k-means (SPLMKM) clustering method. As a non-trivial contribution, we present the solution based on alternating minimization strategy. The comparative experiments reveal the benefit of our proposed method.