Graph Variational Multi-View Clustering

Multi-view clustering (MVC) aims to extract consensus information from multi-source data and has developed rapidly. Although generative model-based methods perform well by leveraging predefined priors, they often overlook inter-instance relationships, which are essential for high-quality clustering. To address this issue, we propose Graph Variational Multi-view Clustering (GVMVC), which integrates graph information into the generative process. Specifically, we treat the original multi-view features and the graph information from each view as observed data to guide the learning of latent representations. The key principles of our approach are: 1) enhancing discriminative feature learning through graph integration; and 2) ensuring consistent multi-view learning via graph-based constraints. Extensive experiments show that GVMVC outperforms state-of-the-art methods across various datasets and metrics.