Information-Theoretic Compressive Measurement Design

Liming Wang, Minhua Chen, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

An information-theoretic projection design framework is proposed, of interest for feature design and compressive measurements. Both Gaussian and Poisson measurement models are considered. The gradient of a proposed information-theoretic metric (ITM) is derived, and a gradient-descent algorithm is applied in design; connections are made to the information bottleneck. The fundamental solution structure of such design is revealed in the case of a Gaussian measurement model and arbitrary input statistics. This new theoretical result reveals how ITM parameter settings impact the number of needed projection measurements, with this verified experimentally. The ITM achieves promising results on real data, for both signal recovery and classification.
Original languageEnglish (US)
Pages (from-to)1150-1164
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume39
Issue number6
DOIs
StatePublished - Jun 1 2017
Externally publishedYes

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