Information-Theoretic Compressive Measurement Design.

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Wang, L; Chen, M; Rodrigues, M; Wilcox, D; Calderbank, R; Carin, L

Published Date

  • June 2017

Published In

Volume / Issue

  • 39 / 6

Start / End Page

  • 1150 - 1164

PubMed ID

  • 27187951

Electronic International Standard Serial Number (EISSN)

  • 1939-3539

International Standard Serial Number (ISSN)

  • 0162-8828

Digital Object Identifier (DOI)

  • 10.1109/tpami.2016.2568189

Language

  • eng