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

Liming Wang, Francesco Renna, Xin Yuan, Miguel Rodrigues, Robert Calderbank, Lawrence Carin

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

3 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4239-4243
Number of pages5
ISBN (Print)9781479999880
DOIs
StatePublished - May 18 2016
Externally publishedYes

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