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Information-Theoretic Compressive Measurement Design

Wang, L; Chen, M; Rodrigues, M; Wilcox, D; Calderbank, R; Carin, L; (2017) Information-Theoretic Compressive Measurement Design. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39 (6) pp. 1150-1164. 10.1109/TPAMI.2016.2568189. Green open access

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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.

Type: Article
Title: Information-Theoretic Compressive Measurement Design
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TPAMI.2016.2568189
Publisher version: http://doi.org/10.1109/TPAMI.2016.2568189
Language: English
Additional information: Open Access article.This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Information-theoretic metric, information bottleneck, projection design, gradient of mutual information, compressive sensing
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1531855
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