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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2568189
|2 doi
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|a eng
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|a Wang, Liming
|e verfasserin
|4 aut
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| 245 |
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|a Information-Theoretic Compressive Measurement Design
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|c 2017
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|a Text
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|a ƒaComputermedien
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|a Date Completed 25.10.2018
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|a Date Revised 25.10.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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
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|a Journal Article
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|a Chen, Minhua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Rodrigues, Miguel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wilcox, David
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Calderbank, Robert
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Carin, Lawrence
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 6 vom: 17. Juni, Seite 1150-1164
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|x 1939-3539
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|g volume:39
|g year:2017
|g number:6
|g day:17
|g month:06
|g pages:1150-1164
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|u http://dx.doi.org/10.1109/TPAMI.2016.2568189
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