Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation

• URL: http://arxiv.org/abs/2106.08537v1
• Date: Wed, 16 Jun 2021 03:34:14 GMT
• Title: Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation
• Authors: Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
• Abstract summary: We study the problem of list-decodable mean estimation, where an adversary can corrupt a majority of the dataset. We develop new algorithms for list-decodable mean estimation, achieving nearly-optimal statistical guarantees.
• Score: 58.24280149662003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of list-decodable mean estimation, where an adversary can corrupt a majority of the dataset. Specifically, we are given a set $T$ of $n$ points in $\mathbb{R}^d$ and a parameter $0< \alpha <\frac 1 2$ such that an $\alpha$-fraction of the points in $T$ are i.i.d. samples from a well-behaved distribution $\mathcal{D}$ and the remaining $(1-\alpha)$-fraction of the points are arbitrary. The goal is to output a small list of vectors at least one of which is close to the mean of $\mathcal{D}$. As our main contribution, we develop new algorithms for list-decodable mean estimation, achieving nearly-optimal statistical guarantees, with running time $n^{1 + o(1)} d$. All prior algorithms for this problem had additional polynomial factors in $\frac 1 \alpha$. As a corollary, we obtain the first almost-linear time algorithms for clustering mixtures of $k$ separated well-behaved distributions, nearly-matching the statistical guarantees of spectral methods. Prior clustering algorithms inherently relied on an application of $k$-PCA, thereby incurring runtimes of $\Omega(n d k)$. This marks the first runtime improvement for this basic statistical problem in nearly two decades. The starting point of our approach is a novel and simpler near-linear time robust mean estimation algorithm in the $\alpha \to 1$ regime, based on a one-shot matrix multiplicative weights-inspired potential decrease. We crucially leverage this new algorithmic framework in the context of the iterative multi-filtering technique of Diakonikolas et. al. '18, '20, providing a method to simultaneously cluster and downsample points using one-dimensional projections --- thus, bypassing the $k$-PCA subroutines required by prior algorithms.

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