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arXiv · 2003.03888

Nearly Optimal Clustering Risk Bounds for Kernel K-Means

Yong Liu·Lizhong Ding·Weiping Wang
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Abstract

In this paper, we study the statistical properties of kernel $k$-means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in the existing clustering risk analyses. We further analyze the statistical effect of computational approximations of the Nystr\"{o}m kernel $k$-means, and prove that it achieves the same statistical accuracy as the exact kernel $k$-means considering only $\Omega(\sqrt{nk})$ Nystr\"{o}m landmark points. To the best of our knowledge, such sharp excess clustering risk bounds for kernel (or approximate kernel) $k$-means have never been proposed before.

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Yong Liu, Lizhong Ding, Weiping Wang. 2020-03-09. Nearly Optimal Clustering Risk Bounds for Kernel K-Means. https://arxiv.org/abs/2003.03888

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