Instance-level Weighted Graph Learning for Incomplete Multi-view Clustering

Incomplete multi-view clustering has attracted board attention due to the frequent absent of some views of real-world objects. Existing incomplete multi-view clustering methods usually assign different weights to different views to learn the consensus graph of multi-views, which however cannot preserve properly the non-noise information in the views of lower weight. In this paper, unlike existing view-level weighted graph learning, we propose a simple yet effective instance-level weighted graph learning for incomplete multi-view clustering. Specifically, we first use the similarity information of the available views to estimate and recover the missing views, such that the harmful impact of the missing views can be reduced. Then, we adaptively assign the weights to the similarities between different perspectives such that negative effects of noises are reduced. Finally, by combining graph fusion and rank constraints, we can learn a new consensus representation of multi-view data for incomplete multi-view analysis. Experimental results on five widely used incomplete multi-view datasets clearly demonstrate the effectiveness of our proposed method.