Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown promise by synthesizing diverse clustering results into a unified consensus. However, current LFMVC methods struggle with noisy and redundant partitions and often fail to capture high-order correlations across views. To address these limitations, we present a novel theoretical framework for analyzing the generalization error bounds of multiple kernel k-means, leveraging local Rademacher complexity and principal eigenvalue proportions. Our analysis establishes a convergence rate of O(1/n), significantly improving upon the existing rate in the order of O(sqrt(k/n)). Building on this insight, we propose a low-pass graph filtering strategy within a multiple linear K-means framework to mitigate noise and redundancy, further refining the principal eigenvalue proportion and enhancing clustering accuracy. Experimental results on benchmark datasets confirm that our approach outperforms state-of-the-art methods in clustering performance and robustness.

As data sources and modalities become more diverse, existing multi-view graph clustering methods face high computational complexity, hindering scalability. Bipartite graph clustering overcomes this by using anchors to build association graphs, reducing complexity to linear time. However, the quality of bipartite graphs and the neglect of higher-order correlations remain significant limitations. To tackle these challenges, we propose MCBGF, a scalable bipartite graph-based multi-view clustering method enhanced by consensus graph filtering. By integrating the original bipartite structure to counteract degradation from over-smoothing in high-order filters, MCBGF achieves robust and consistent performance. With linear time complexity, MCBGF efficiently processes large-scale datasets and consistently outperforms state-of-the-art methods in experimental evaluations. The code has been released at https://github.com/sxuHui/MCBGF.