Publications by Galen Reeves.

Papers Published

  1. Renna, F; Wang, L; Yuan, X; Yang, J; Reeves, G; Calderbank, R; Carin, L; Rodrigues, MRD, Classification and reconstruction of compressed GMM signals with side information, Ieee International Symposium on Information Theory Proceedings, vol. 2015-June (September, 2015), pp. 994-998 [doi] .
    (last updated on 2021/05/06)

    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.