Consensus and complementary regularized non-negative matrix factorization for multi-view image clustering

Abstract

Multi-view clustering (MVC) aims to classify objects using multi-view data. Many existing MVC methods explore the consensus agreement among views or enhance the complementary features from multiple views in order to improve the clustering quality. However, multi-view data, especially multi-view image data, contains much more complex and meaningful information, which not only describes intrinsic common properties of one object, but also implies rich unique features from different views. Both of them are important and useful for MVC. Though the widely-used non-negative matrix factorization (NMF) based MVC methods can address these two kinds of information, the qualities of learned representations are not well, which limits the clustering performance. To solve this problem, motivated by the ideas of feature selection and graph regularization, a novel NMF based unified representation learning framework is presented with the integration of two specifically designed graph regularization terms in order to obtain a high-quality representation for multi-view data: An unified NMF based optimization problem is formulated to learn the consensus and complementary representations simultaneously and an alternating optimization algorithm is designed to solve this non-convex optimization problem. Then, these learned representations are efficiently fused to create an integrated graph representation for the final clustering. Extensive experiments on six real image datasets demonstrate the superior performance of the proposed method compared with other eleven competitive MVC methods.