Classification and reconstruction of compressed GMM signals with side information

Francesco Renna, Liming Wang, Xin Yuan, Jianbo Yang, Galen Reeves, Robert Calderbank, Lawrence Carin, Miguel R.D. Rodrigues

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Information Theory - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages994-998
Number of pages5
ISBN (Print)9781467377041
DOIs
StatePublished - Sep 28 2015
Externally publishedYes

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