Reliable Multi-View Clustering With Graph Neural Network

Abstract

Deep multi-view clustering (MVC) typically leverages deep neural networks to extract consistency and complementarity information across multiple views. As a representative approach, graph neural networks (GNN) gather significant attention due to its ability to model both topology and feature simultaneously, making them highly attractive for researchers working on multi-view scenarios. However, existing GNN-based MVC algorithms often overlook the inherent heterogeneity and noise in multi-view data, which introduces uncertainty into the embedding learning process. Moreover, the message passing mechanism of GNN may further amplify this uncertainty, leading to unreliable embeddings and poor clustering performance. To address the issues, we propose a model called Reliable Multi-View Clustering with Graph Neural Network (RM-GNN). The proposed model first uses view-specific graph convolution encoders to project features of different views into unified semantic space. Then, we introduce an adaptive fusion method that effectively combines the representations obtained from each view. Furthermore, we propose a similarity-driven evidence learning module to effectively identify and distinguish unreliable samples in the embedding space. Finally, we introduce a unified transformation matrix to enforce global topological consistency. Extensive experimental results on nine benchmark datasets demonstrate that our algorithm generally outperforms state-of-the-art methods.