Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion

• URL: http://arxiv.org/abs/2412.18207v1
• Date: Tue, 24 Dec 2024 06:24:08 GMT
• Title: Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion
• Authors: Liang Du, Henghui Jiang, Xiaodong Li, Yiqing Guo, Yan Chen, Feijiang Li, Peng Zhou, Yuhua Qian,
• Abstract summary: Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. We present a novel theoretical framework for analyzing the generalization error bounds of multiple kernel $k$-means. We propose a low-pass graph filtering strategy within a multiple linear $k$-means framework to mitigate noise and redundancy.
• Score: 19.46433323866636
• License:
• 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 $\mathcal{O}(1/n)$, significantly improving upon the existing rate in the order of $\mathcal{O}(\sqrt{k/n})$. 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. The related codes is available at https://github.com/csliangdu/GMLKM .

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