A general framework for reconstruction and classification from compressive measurements with side information

Liming Wang; Francesco Renna; Xin Yuan; Miguel Rodrigues; Robert Calderbank; Lawrence Carin

doi:10.1109/ICASSP.2016.7472476

A general framework for reconstruction and classification from compressive measurements with side information

Liming Wang, Francesco Renna, Xin Yuan, Miguel Rodrigues, Robert Calderbank, Lawrence Carin

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

3 Scopus citations

Abstract

We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally.

Original language	English (US)
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4239-4243
Number of pages	5
ISBN (Print)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472476
State	Published - May 18 2016
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2021-02-09

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10.1109/ICASSP.2016.7472476

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Wang, L., Renna, F., Yuan, X., Rodrigues, M., Calderbank, R., & Carin, L. (2016). A general framework for reconstruction and classification from compressive measurements with side information. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 4239-4243). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472476

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title = "A general framework for reconstruction and classification from compressive measurements with side information",

abstract = "We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally.",

author = "Liming Wang and Francesco Renna and Xin Yuan and Miguel Rodrigues and Robert Calderbank and Lawrence Carin",

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doi = "10.1109/ICASSP.2016.7472476",

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Wang, L, Renna, F, Yuan, X, Rodrigues, M, Calderbank, R & Carin, L 2016, A general framework for reconstruction and classification from compressive measurements with side information. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc., pp. 4239-4243. https://doi.org/10.1109/ICASSP.2016.7472476

A general framework for reconstruction and classification from compressive measurements with side information. / Wang, Liming; Renna, Francesco; Yuan, Xin et al.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 4239-4243.

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

TY - GEN

T1 - A general framework for reconstruction and classification from compressive measurements with side information

AU - Wang, Liming

AU - Renna, Francesco

AU - Yuan, Xin

AU - Rodrigues, Miguel

AU - Calderbank, Robert

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2016/5/18

Y1 - 2016/5/18

N2 - We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally.

AB - We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally.

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DO - 10.1109/ICASSP.2016.7472476

M3 - Conference contribution

SN - 9781479999880

SP - 4239

EP - 4243

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Wang L, Renna F, Yuan X, Rodrigues M, Calderbank R, Carin L. A general framework for reconstruction and classification from compressive measurements with side information. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 4239-4243 doi: 10.1109/ICASSP.2016.7472476

A general framework for reconstruction and classification from compressive measurements with side information

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