A general framework for reconstruction and classification from compressive measurements with side information

Conference Paper

We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally.

Full Text

Duke Authors

Cited Authors

  • Wang, L; Renna, F; Yuan, X; Rodrigues, M; Calderbank, R; Carin, L

Published Date

  • May 18, 2016

Published In

Volume / Issue

  • 2016-May /

Start / End Page

  • 4239 - 4243

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 13 (ISBN-13)

  • 9781479999880

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2016.7472476

Citation Source

  • Scopus