Sharper Error Bounds in Late Fusion Multi-view Clustering with Eigenvalue Proportion Optimization
Liang Du, Henghui Jiang, Xiaodong Li, Yiqing Guo, Yan Chen, Feijiang Li, Peng Zhou, Yuhua Qian
Abstract
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.
BibTeX
@article{Du_Jiang_Li_Guo_Chen_Li_Zhou_Qian_2025, title={Sharper Error Bounds in Late Fusion Multi-view Clustering with Eigenvalue Proportion Optimization}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33799}, DOI={10.1609/aaai.v39i15.33799}, abstractNote={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.}, number={15}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Du, Liang and Jiang, Henghui and Li, Xiaodong and Guo, Yiqing and Chen, Yan and Li, Feijiang and Zhou, Peng and Qian, Yuhua}, year={2025}, month={Apr.}, pages={16381-16388} }