Classification and reconstruction of compressed GMM signals with side information

Conference Paper

This paper offers a characterization of performance limits for classification and reconstruction of high-dimensional signals from noisy compressive measurements, in the presence of side information. We assume the signal of interest and the side information signal are drawn from a correlated mixture of distributions/components, where each component associated with a specific class label follows a Gaussian mixture model (GMM). We provide sharp sufficient and/or necessary conditions for the phase transition of the misclassification probability and the reconstruction error in the low-noise regime. These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of measurements taken from the signal of interest, the number of measurements taken from the side information signal, and the geometry of these signals and their interplay.

Full Text

Duke Authors

Cited Authors

  • Renna, F; Wang, L; Yuan, X; Yang, J; Reeves, G; Calderbank, R; Carin, L; Rodrigues, MRD

Published Date

  • September 28, 2015

Published In

Volume / Issue

  • 2015-June /

Start / End Page

  • 994 - 998

International Standard Serial Number (ISSN)

  • 2157-8095

International Standard Book Number 13 (ISBN-13)

  • 9781467377041

Digital Object Identifier (DOI)

  • 10.1109/ISIT.2015.7282604

Citation Source

  • Scopus