Self‐inferring incomplete multi‐view clustering

Abstract

AbstractWith the advantage of exploiting complementary and consensus information across multiple views, techniques for Multi‐view Clustering have attracted increasing attention in recent years. However, it is common that data on some views is not completed in real‐world applications, which brings the challenge of partial mapping between the views. To explore the information hidden in the local geometric structure and recover missing instances through mining the information hidden in existing instances, a self‐inferring incomplete multi‐view clustering algorithm is proposed. Firstly, the incomplete multi‐view data is replenished directly and exploited as variables for inferring the missing instances. And then, a feature graph constraint is united in consensus learning. Besides, a similarity graph learning method is imposed to preserve the local manifold structure. At last, the inferred instances are filled in the missing instances for learning better consensus representation in the iterative process. Extensive experiment results show that this method can improve the clustering performance compared with the state‐of‐the‐art methods.