Summary of Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion, by Liang Du et al.
Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion
by Liang Du, Henghui Jiang, Xiaodong Li, Yiqing Guo, Yan Chen, Feijiang Li, Peng Zhou, Yuhua Qian
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel theoretical framework for analyzing the generalization error bounds of multiple kernel k-means, leveraging local Rademacher complexity and principal eigenvalue proportions. The approach establishes a convergence rate of O(1/n), improving upon existing rates in the order of O(sqrt(k/n)). Building on this insight, the authors propose a low-pass graph filtering strategy within a multiple linear k-means framework to mitigate noise and redundancy, refining clustering accuracy. Experimental results confirm that the approach outperforms state-of-the-art methods in clustering performance and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves clustering by combining information from multiple views. Current approaches struggle with noisy and redundant partitions and often miss important connections between views. The authors develop a new way to analyze this problem, showing how their method can accurately group data points together while ignoring noise and redundancy. They test their approach on benchmark datasets and show that it outperforms other methods in clustering quality and robustness. |
Keywords
» Artificial intelligence » Clustering » Generalization » K means
