Learning Smooth Representation for Multi-view Subspace Clustering

Multi-view subspace clustering aims to exploit data correlation consensus among multiple views, which essentially can be treated as graph-based approach. However, existing methods usually suffer from suboptimal solution as the raw data might not be separable into subspaces. In this paper, we propose to achieve a smooth representation for each view and thus facilitate the downstream clustering task. It is based on a assumption that a graph signal is smooth if nearby nodes on the graph have similar features representations. Specifically, our mode is able to retain the graph geometric features by applying a low-pass filter to extract the smooth representations of multiple views. Besides, our method achieves the smooth representation learning as well as multi-view clustering interactively in a unified framework, hence it is an end-to-end single-stage learning problem. Substantial experiments on benchmark multi-view datasets are performed to validate the effectiveness of the proposed method, compared to the state-of-the-arts over the clustering performance.