Incomplete Multi-View Clustering Via Inference and Evaluation

Abstract

Multi-view clustering aims to improve the clustering performance by leveraging information from multiple views. Most existing works assume that all views are complete. However, samples in real-world scenarios cannot be always observed in all views, leading to the challenging problem of Incomplete Multi-View Clustering (IMVC). Although some attempts are made recently, they still suffer from the following two limitations: (1) they usually adopt shallow models, which are unable to sufficiently explore the consistency and complementary of multiple views; (2) they lack of a suitable measurement to evaluate the quality of the recovered data during the learning process. To address the aforementioned limitations, we introduce a novel Incomplete Multi-View Clustering via Inference and Evaluation (IMVC-IE). Specifically, IMVC-IE adopts the contrastive learning strategy on features of different views to excavate the underlying information from existing samples firstly. Subsequently, massive alternative simulated data are inferred for missing views and a novel evaluation strategy is presented to obtain the proper data for missing views completion. Extensive experiments are conducted and verify the effectiveness of our method.