Incomplete multi-view clustering via structure exploration and missing-view inference

Abstract

Incomplete multi-view clustering (IMVC) aims to boost clustering performance by capturing complementary information from incomplete multi-views, where partial data samples in one or more views are missing. Current IMVC methods mostly impute missing samples at the guidance of the global/local structure or directly learn a common representation without imputation using subspace or graph learning techniques. However, the consistent and inconsistent structures across views are often ignored during imputation, leading to the introduction of noise and biases. Additionally, lacking the handling of missing samples would mislead the learning methods and degrade clustering performance. To this end, we propose a novel approach called Structure Exploration and Missing-view Inference (SEMI) for IMVC. Specifically, SEMI explores the underlying multi-structures of data, including global, local, consistent, and inconsistent structures, by jointly modeling self-expression subspace, graph, and clustering-oriented partition learning. This enables the capture of consistent and discriminative information and fuses it into a unified coefficient matrix. The learned coefficient matrix with the explored multi-structures then guides the inference of missing views, facilitating the alleviation of the influence of existing noise and biases and the mitigation of the introduction of further noise and biases. These two components are seamlessly integrated and mutually improved through an efficient alternating optimization strategy. Experimental results demonstrate the effectiveness and superior performance of the proposed method.