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Bayesian compressive sensing and projection optimization

Publication ,  Journal Article
Ji, S; Carin, L
Published in: ACM International Conference Proceeding Series
August 23, 2007

This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K << N real measurements. This is achieved under the assumption that the underlying signal has a sparse representation in some basis (e.g., wavelets). In this paper we demonstrate how techniques developed in machine learning, specifically sparse Bayesian regression and active learning, may be leveraged to this new problem. We also point out future research directions in compressive sensing of interest to the machine-learning community.

Duke Scholars

Published In

ACM International Conference Proceeding Series

DOI

Publication Date

August 23, 2007

Volume

227

Start / End Page

377 / 384
 

Citation

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Chicago
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Ji, S., & Carin, L. (2007). Bayesian compressive sensing and projection optimization. ACM International Conference Proceeding Series, 227, 377–384. https://doi.org/10.1145/1273496.1273544
Ji, S., and L. Carin. “Bayesian compressive sensing and projection optimization.” ACM International Conference Proceeding Series 227 (August 23, 2007): 377–84. https://doi.org/10.1145/1273496.1273544.
Ji S, Carin L. Bayesian compressive sensing and projection optimization. ACM International Conference Proceeding Series. 2007 Aug 23;227:377–84.
Ji, S., and L. Carin. “Bayesian compressive sensing and projection optimization.” ACM International Conference Proceeding Series, vol. 227, Aug. 2007, pp. 377–84. Scopus, doi:10.1145/1273496.1273544.
Ji S, Carin L. Bayesian compressive sensing and projection optimization. ACM International Conference Proceeding Series. 2007 Aug 23;227:377–384.

Published In

ACM International Conference Proceeding Series

DOI

Publication Date

August 23, 2007

Volume

227

Start / End Page

377 / 384