Structural Deep Incomplete Multi-view Clustering Network
Jie Wen, Zhihao Wu, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu
Abstract
In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.