Incomplete Multi-View Clustering via Auto-Weighted Fusion in Partition Space

As a class of effective methods for incomplete multi-view clustering, graph-based algorithms have recently drawn wide attention. However, most of them could use further improvement regarding the following aspects. First, in some graph-based models, all views are forced to share a common similarity graph regardless of the severe consistency degeneration due to incomplete views. Next, similarity graph construction and cluster analysis are sometimes performed separately. Finally, the contribution difference of individual views is not always carefully considered. To address these issues simultaneously, this paper proposes an incomplete multi-view clustering algorithm based on auto-weighted fusion in partition space. In our algorithm, the information of cluster structure is introduced into the process of similarity learning to construct a desirable similarity graph, information fusion is performed in partition space to alleviate the negative impact brought about by consistency degradation, and all views are adaptively weighted to reflect their different contributions to clustering tasks. Finally, all the subtasks are collaboratively optimized in a united framework to reach an overall optimal result. Experimental results show that the proposed method compares favorably with the state-of-the-art methods.