Risk Bounds For Mode Clustering

Martin Azizyan; Yen-Chi Chen; Aarti Singh; Larry Wasserman

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arXiv · 1505.00482

Risk Bounds For Mode Clustering

Martin Azizyan·Yen-Chi Chen·Aarti Singh·Larry Wasserman

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Abstract

Density mode clustering is a nonparametric clustering method. The clusters are the basins of attraction of the modes of a density estimator. We study the risk of mode-based clustering. We show that the clustering risk over the cluster cores --- the regions where the density is high --- is very small even in high dimensions. And under a low noise condition, the overall cluster risk is small even beyond the cores, in high dimensions.

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math.ST cs.LG stat.ML stat.TH

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Martin Azizyan, Yen-Chi Chen, Aarti Singh, Larry Wasserman. 2015-05-03. Risk Bounds For Mode Clustering. https://arxiv.org/abs/1505.00482

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