Conditional central limit theorems for Gaussian projections

Conference Paper

© 2017 IEEE. This paper addresses the question of when projections of a high-dimensional random vector are approximately Gaussian. This problem has been studied previously in the context of high-dimensional data analysis, where the focus is on low-dimensional projections of high-dimensional point clouds. The focus of this paper is on the typical behavior when the projections are generated by an i.i.d. Gaussian projection matrix. The main results are bounds on the deviation between the conditional distribution of the projections and a Gaussian approximation, where the conditioning is on the projection matrix. The bounds are given in terms of the quadratic Wasserstein distance and relative entropy and are stated explicitly as a function of the number of projections and certain key properties of the random vector. The proof uses Talagrand's transportation inequality and a general integral-moment inequality for mutual information. Applications to random linear estimation and compressed sensing are discussed.

Full Text

Duke Authors

Cited Authors

  • Reeves, G

Published Date

  • August 9, 2017

Published In

Start / End Page

  • 3045 - 3049

International Standard Serial Number (ISSN)

  • 2157-8095

International Standard Book Number 13 (ISBN-13)

  • 9781509040964

Digital Object Identifier (DOI)

  • 10.1109/ISIT.2017.8007089

Citation Source

  • Scopus