One-Stage Partition-Fusion Multi-view Subspace Clustering Algorithm

Abstract

Multi-view subspace clustering has attracted increasing attention for revealing the inherent low-dimension structure of the data. Nevertheless, most existing methods directly fuse the multiple noisy affinity matrices from the original data, and commonly conduct clustering after obtaining a unified multi-view representation. Separating the representation learning from the clustering process can result in a suboptimal clustering result. To this end, this paper proposes a one-stage partition-fusion multi-view subspace clustering algorithm. Instead of directly fusing the noisy and redundant affinity matrices, this paper fuses the more discriminative partition-level information extracted from the affinity matrices. Moreover, this paper proposes a new framework, integrating representation learning, multiple information fusion and final clustering process. The three sub-processes promote each other to serve clustering best. The promising clustering results can lead to better representations and therefore better clustering performance. Consequently, this paper solves the resultant optimization problem through an alternative algorithm. Experiment results on four real-world benchmark datasets show the effectiveness and superior performance of the proposed method over the state-of-the-art approaches.

Keywords

• multi-view clustering;
• subspace clustering;
• data fusion;
• partition fusion