Robust k-means clustering for distributions with two moments

Annals of Statistics. 2021. Vol. 49. No. 4. P. 2206–2230.

Klochkov Y., Kroshnin A., Zhivotovskiy N.

We consider the robust algorithms for the k-means clustering problem where a quantizer is constructed based on N independent observations. Our main results are median of means based nonasymptotic excess distortion bounds that hold under the two bounded moments assumption in a general separable Hilbert space. In particular, our results extend the renowned asymptotic result of (Ann. Statist. 9 (1981) 135–140) who showed that the existence of two moments is sufficient for strong consistency of an empirically optimal quantizer in Rd. In a special case of clustering in Rd, under two bounded moments, we prove matching (up to constant factors) nonasymptotic upper and lower bounds on the excess distortion, which depend on the probability mass of the lightest cluster of an optimal quantizer. Our bounds have the sub-Gaussian form, and the proofs are based on the versions of uniform bounds for robust mean estimators.

Keywords: k-means, clustering, robust estimation

Publication based on the results of: Uncertainty quantification in machine learning algorithms (2021)