Cluster-Aware Contrastive Multi-View Clustering Based on Masked Views

In this paper, we present a novel Self-Supervised Learning (SSL) framework tailored for Multi-View Clustering (MVC), which learns cross-view semantic representations with clear clustering boundaries and derives balanced clustering in an end-to-end manner. Concretely, we propose a generative SSL module that learns high-level semantic representations by recovering randomly masked views from observed views. Then the extracted representations are unified via a sample-level local fusion mechanism and projected into a unit-hypersphere space with evenly distributed cluster prototypes such that the pseudo labels can be directly retrieved using cosine similarity. For each sample, we define highly credible positive pairs of the same cluster and negative pairs of different clusters and design a contrastive SSL module to force the sample to move toward its cluster prototype while farther from the other prototypes in the embedding space. Consequently, the representations exhibit clearer clustering boundaries, and the two SSL modules benefit each other. Finally, we further introduce a clustering regularizer to prevent trivial solutions and derive balanced clustering with theoretical guarantees. Comprehensive evaluations over eight benchmark datasets validate the effectiveness of our proposals against ten state-of-the-art MVC methods.