Adapted statistical compressive sensing: Learning to sense gaussian mixture models

Julio M. Duarte-Carvajalino; Guoshen Yu; Lawrence Carin; Guillermo Sapiro

doi:10.1109/ICASSP.2012.6288708

Adapted statistical compressive sensing: Learning to sense gaussian mixture models

Julio M. Duarte-Carvajalino, Guoshen Yu, Lawrence Carin, Guillermo Sapiro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

A framework for learning sensing kernels adapted to signals that follow a Gaussian mixture model (GMM) is introduced in this paper. This follows the paradigm of statistical compressive sensing (SCS), where a statistical model, a GMM in particular, replaces the standard sparsity model of classical compressive sensing (CS), leading to both theoretical and practical improvements. We show that the optimized sensing matrix outperforms random sampling matrices originally exploited both in CS and SCS.

Original language	English (US)
Title of host publication	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages	3653-3656
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2012.6288708
State	Published - 2012
Externally published	Yes
Event	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan Duration: Mar 25 2012 → Mar 30 2012

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Other

Other	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/Territory	Japan
City	Kyoto
Period	3/25/12 → 3/30/12

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Keywords

Compressive Sensing
Gaussian Mixture Models
Learning
Structured Sparsity

Access to Document

10.1109/ICASSP.2012.6288708

Cite this

Duarte-Carvajalino, J. M., Yu, G., Carin, L., & Sapiro, G. (2012). Adapted statistical compressive sensing: Learning to sense gaussian mixture models. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 3653-3656). Article 6288708 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6288708

Duarte-Carvajalino, Julio M. ; Yu, Guoshen ; Carin, Lawrence et al. / Adapted statistical compressive sensing : Learning to sense gaussian mixture models. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. pp. 3653-3656 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{58da5ab0564e4f8593f6bb0236cefae7,

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abstract = "A framework for learning sensing kernels adapted to signals that follow a Gaussian mixture model (GMM) is introduced in this paper. This follows the paradigm of statistical compressive sensing (SCS), where a statistical model, a GMM in particular, replaces the standard sparsity model of classical compressive sensing (CS), leading to both theoretical and practical improvements. We show that the optimized sensing matrix outperforms random sampling matrices originally exploited both in CS and SCS.",

keywords = "Compressive Sensing, Gaussian Mixture Models, Learning, Structured Sparsity",

author = "Duarte-Carvajalino, {Julio M.} and Guoshen Yu and Lawrence Carin and Guillermo Sapiro",

year = "2012",

doi = "10.1109/ICASSP.2012.6288708",

language = "English (US)",

isbn = "9781467300469",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "3653--3656",

booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",

note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",

}

Duarte-Carvajalino, JM, Yu, G, Carin, L & Sapiro, G 2012, Adapted statistical compressive sensing: Learning to sense gaussian mixture models. in 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings., 6288708, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 3653-3656, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, Kyoto, Japan, 3/25/12. https://doi.org/10.1109/ICASSP.2012.6288708

Adapted statistical compressive sensing: Learning to sense gaussian mixture models. / Duarte-Carvajalino, Julio M.; Yu, Guoshen; Carin, Lawrence et al.
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 3653-3656 6288708 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Adapted statistical compressive sensing

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AU - Sapiro, Guillermo

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AB - A framework for learning sensing kernels adapted to signals that follow a Gaussian mixture model (GMM) is introduced in this paper. This follows the paradigm of statistical compressive sensing (SCS), where a statistical model, a GMM in particular, replaces the standard sparsity model of classical compressive sensing (CS), leading to both theoretical and practical improvements. We show that the optimized sensing matrix outperforms random sampling matrices originally exploited both in CS and SCS.

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KW - Gaussian Mixture Models

KW - Learning

KW - Structured Sparsity

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DO - 10.1109/ICASSP.2012.6288708

M3 - Conference contribution

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Duarte-Carvajalino JM, Yu G, Carin L, Sapiro G. Adapted statistical compressive sensing: Learning to sense gaussian mixture models. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 3653-3656. 6288708. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2012.6288708

Adapted statistical compressive sensing: Learning to sense gaussian mixture models

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All Science Journal Classification (ASJC) codes

Keywords

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