Multi-view Subspace Clustering via Partition Fusion

Abstract Multi-view clustering is an important approach for analyzing multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Essentially, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final results. However, its performance may degrade due to noises existing in each individual view or inconsistencies between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find a shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.