Heat Kernel Diffusion for Enhanced Late Fusion Multi-View Clustering

Recent advancements in Multi-view Clustering (MVC) have highlighted the benefits of late fusion techniques. However, existing late fusion-based MVC (LFMVC) approaches often struggle with intrinsic noise and redundancy within base clustering embeddings generated by traditional method, and fail to capture higher-order correlations among samples and across views. We propose a novel method that incorporates an optimal consensus heat kernel-induced graph filter to address these issues. Our approach leverages diffusion processes to construct filters that capture high-order information, achieving neighborhood smoothing while maintaining local consistency within each view. By integrating a consensus graph filter with downstream clustering tasks within a unified learning framework, our method enhances performance through mutual reinforcement. In particular, our method does not require additional hyperparameter tuning, simplifying the optimization process. Experimental results on various benchmark datasets demonstrate the superior effectiveness of our approach, outperforming state-of-the-art methods in terms of clustering performance.