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

Publication ,  Conference
Wang, L; Renna, F; Yuan, X; Rodrigues, M; Calderbank, R; Carin, L
Published in: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
May 18, 2016

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.

Duke Scholars

Published In

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

DOI

ISSN

1520-6149

ISBN

9781479999880

Publication Date

May 18, 2016

Volume

2016-May

Start / End Page

4239 / 4243
 

Citation

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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 (Vol. 2016-May, pp. 4239–4243). https://doi.org/10.1109/ICASSP.2016.7472476
Wang, L., F. Renna, X. Yuan, M. Rodrigues, R. Calderbank, and L. Carin. “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, 2016-May:4239–43, 2016. https://doi.org/10.1109/ICASSP.2016.7472476.
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. 2016. p. 4239–43.
Wang, L., et al. “A general framework for reconstruction and classification from compressive measurements with side information.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2016-May, 2016, pp. 4239–43. Scopus, doi:10.1109/ICASSP.2016.7472476.
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. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2016. p. 4239–4243.

Published In

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

DOI

ISSN

1520-6149

ISBN

9781479999880

Publication Date

May 18, 2016

Volume

2016-May

Start / End Page

4239 / 4243