Self-Paced Multi-View Clustering via a Novel Soft Weighted Regularizer

Multi-view clustering (MVC), which can exploit complementary information of different views to enhance the clustering performance, has attracted people's increasing attentions in recent years. However, existing multi-view clustering methods typically solve a non-convex problem, therefore are easily stuck into bad local minima. In addition, noisy data and outliers affect the clustering process negatively. In this paper, we propose self-paced multi-view clustering via a novel soft weighted regularizer (SPMVC) to address these issues. Specifically, SPMVC progressively selects samples to train the MVC model from simplicity to complexity in a self-paced manner. A novel soft weighted regularizer is proposed to further reduce the negative impact of outliers and noisy data. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method.