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Information-Theoretic Compressive Measurement Design.

Publication ,  Journal Article
Wang, L; Chen, M; Rodrigues, M; Wilcox, D; Calderbank, R; Carin, L
Published in: IEEE transactions on pattern analysis and machine intelligence
June 2017
Published version (DOI)

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.

Duke Scholars

Robert Calderbank
Author Robert Calderbank Computer Science
Lawrence Carin
Author Lawrence Carin Electrical and Computer Engineering
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Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

10.1109/tpami.2016.2568189

EISSN

1939-3539

ISSN

0162-8828

Publication Date

June 2017

Volume

39

Issue

6

Start / End Page

1150 / 1164

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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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), 1150–1164. https://doi.org/10.1109/tpami.2016.2568189
Wang, Liming, Minhua Chen, Miguel Rodrigues, David Wilcox, Robert Calderbank, and Lawrence Carin. “Information-Theoretic Compressive Measurement Design.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 6 (June 2017): 1150–64. https://doi.org/10.1109/tpami.2016.2568189.
Wang L, Chen M, Rodrigues M, Wilcox D, Calderbank R, Carin L. Information-Theoretic Compressive Measurement Design. IEEE transactions on pattern analysis and machine intelligence. 2017 Jun;39(6):1150–64.
Wang, Liming, et al. “Information-Theoretic Compressive Measurement Design.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, June 2017, pp. 1150–64. Epmc, doi:10.1109/tpami.2016.2568189.
Wang L, Chen M, Rodrigues M, Wilcox D, Calderbank R, Carin L. Information-Theoretic Compressive Measurement Design. IEEE transactions on pattern analysis and machine intelligence. 2017 Jun;39(6):1150–1164.

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

10.1109/tpami.2016.2568189

EISSN

1939-3539

ISSN

0162-8828

Publication Date

June 2017

Volume

39

Issue

6

Start / End Page

1150 / 1164

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing