Deep Discriminative Multi-View Clustering

Multi-view clustering based on deep auto-encoder networks has garnered increasing attention and made significant progress in recent years. However, we argue that most existing methods inadequately explore the discriminability while learning clustering assignments, resulting in models struggling to accurately cluster data, particularly those with ambiguous semantics. To address this problem, we propose a novel framework termed deep discriminative multi-view clustering (DDMvC). This framework is designed to further increase the inter-cluster distances by learning a discriminative projection dictionary with global prior information. To begin with, we enhance the reliability of the dictionary atoms by initializing them with class-specific prototypes derived from concatenated global features across multiple views. Subsequently, we iteratively refine the atoms to guarantee their independence from any specific cluster. Simultaneously, we incorporate contrastive learning for the cluster assignments projected by these atoms, striving for inter-view consistent clustering results. Experimental results on benchmark multi-view datasets demonstrate that our framework achieves the state-of-the-art clustering performance.