Chen, M;
Renna, Francesco;
Rodrigues, Miguel;
(2016)
On the design of linear projections for compressive sensing with side information.
In:
2016 IEEE International Symposium on Information Theory (ISIT).
(pp. pp. 670-674).
IEEE
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Abstract
In this paper, we study the problem of projection kernel design for the reconstruction of high-dimensional signals from low-dimensional measurements in the presence of side information, assuming that the signal of interest and the side information signal are described by a joint Gaussian mixture model (GMM). In particular, we consider the case where the projection kernel for the signal of interest is random, whereas the projection kernel associated to the side information is designed. We then derive sufficient conditions on the number of measurements needed to guarantee that the minimum meansquared error (MMSE) tends to zero in the low-noise regime. Our results demonstrate that the use of a designed kernel to capture side information can lead to substantial gains in relation to a random one, in terms of the number of linear projections required for reliable reconstruction.
| Type: | Proceedings paper |
|---|---|
| Title: | On the design of linear projections for compressive sensing with side information |
| Event: | 2016 IEEE International Symposium on Information Theory (ISIT) |
| Location: | Barcelona, Spain |
| Dates: | 10 July 2016 - 15 July 2016 |
| ISBN-13: | 9781509018062 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISIT.2016.7541383 |
| Publisher version: | http://ieeexplore.ieee.org/document/7541383/ |
| Language: | English |
| Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | Kernel design, side information, Gaussian mixture model (GMM), minimum mean squared error (MMSE) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1530189 |
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