Settling the Polynomial Learnability of Mixtures of Gaussians

TL;DR

This paper presents a polynomial-time algorithm for learning the parameters of Gaussian mixture models in high dimensions, with minimal assumptions and near-optimal clustering and density estimation capabilities.

Contribution

It introduces a novel high-dimensional learning algorithm based on univariate mixture learning and hierarchical clustering, overcoming technical challenges in multivariate Gaussian mixtures.

Findings

Algorithm runs in polynomial time relative to dimension and accuracy

Achieves near-optimal clustering and density estimation for Gaussian mixtures

Proves exponential dependence on the number of Gaussians is necessary

Abstract

Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians. As simple consequences of our learning algorithm, we can perform near-optimal clustering of the sample points and density estimation for mixtures of k Gaussians, efficiently. The building blocks of our algorithm are based on the work Kalai et al. [STOC 2010] that gives an efficient algorithm for learning mixtures of two Gaussians by considering a series of projections down to one dimension, and applying the method of moments to each univariate projection. A major technical hurdle in Kalai et al. is showing that one can efficiently learn univariate mixtures of two Gaussians. In contrast, because pathological scenarios can arise when considering univariate projections of mixtures of more than two Gaussians, the bulk of the work in this paper concerns how to leverage an algorithm for learning univariate mixtures (of many Gaussians) to yield an efficient algorithm for learning in high dimensions. Our algorithm employs hierarchical clustering and rescaling, together with delicate methods for backtracking and recovering from failures that can occur in our univariate algorithm. Finally, while the running time and data requirements of our algorithm depend exponentially on the number of Gaussians in the mixture, we prove that such a dependence is necessary.