Learning mixtures of gaussians in high dimensions

Conference Paper

Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the covariance matrices of these Gaussians. This learning problem arises in many areas ranging from the natural sciences to the social sciences, and has also found many machine learning applications. Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case. In this work, we show that provided we are in high enough dimensions, the class of Gaussian mixtures is learnable in its most general form under a smoothed analysis framework, where the parameters are randomly perturbed from an adversarial starting point. In particular, given samples from a mixture of Gaussians with randomly perturbed parameters, when n ≥ ?(k2), we give an algorithm that learns the parameters with polynomial running time and using polynomial number of samples. The central algorithmic ideas consist of new ways to decompose the moment tensor of the Gaussian mixture by exploiting its structural properties. The symmetries of this tensor are derived from the combinatorial structure of higher order moments of Gaussian distributions (sometimes referred to as Isserlis' theorem or Wick's theorem). We also develop new tools for bounding smallest singular values of structured random matrices, which could be useful in other smoothed analysis settings.

Full Text

Duke Authors

Cited Authors

  • Ge, R; Huang, Q; Kakade, SM

Published Date

  • June 14, 2015

Published In

Volume / Issue

  • 14-17-June-2015 /

Start / End Page

  • 761 - 770

International Standard Serial Number (ISSN)

  • 0737-8017

International Standard Book Number 13 (ISBN-13)

  • 9781450335362

Digital Object Identifier (DOI)

  • 10.1145/2746539.2746616

Citation Source

  • Scopus