Robust multi-view data clustering with multi-view capped-norm K-means

Real-world data sets are often comprised of multiple representations or views which provide different and complementary aspects of information. Multi-view clustering is an important approach to analyze multi-view data in a unsupervised way. Previous studies have shown that better clustering accuracy can be achieved using integrated information from all the views rather than just relying on each view individually. That is, the hidden patterns in data can be better explored by discovering the common latent structure shared by multiple views. However, traditional multi-view clustering methods are usually sensitive to noises and outliers, which greatly impair the clustering performance in practical problems. Furthermore, existing multi-view clustering methods, e.g. graph-based methods, are with high computational complexity due to the kernel/affinity matrix construction or the eigendecomposition. To address these problems, we propose a novel robust multi-view clustering method to integrate heterogeneous representations of data. To make our method robust to the noises and outliers, especially the extreme data outliers, we utilize the capped-norm loss as the objective. The proposed method is of low complexity, and in the same level as the classic K-means algorithm, which is a major advantage for unsupervised learning. We derive a new efficient optimization algorithm to solve the multi-view clustering problem. Finally, extensive experiments on benchmark data sets show that our proposed method consistently outperforms the state-of-the-art clustering methods.