Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing

Bereyhi A, Müller R (2018)


Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2018

Publisher: IEEE

Event location: Alberta CA

DOI: 10.1109/icassp.2018.8462075

Open Access Link: https://arxiv.org/abs/1802.05776

Abstract

This paper studies the asymptotic performance of maximum-a-posteriori estimation in the presence of prior information. The problem arises in several applications such as recovery of signals with non-uniform sparsity pattern from underdetermined measurements. With prior information, the maximum-a-posteriori estimator might have asymmetric penalty. We consider a generic form of this estimator and study its performance via the replica method. Our analyses demonstrate an asymmetric form of the decoupling property in the large-system limit. Employing our results, we further investigate the performance of weighted zero-norm minimization for recovery of a non-uniform sparse signal. Our investigations illustrate that for a given distortion, the minimum number of required measurements can be significantly reduced by choosing weighting coefficients optimally.

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How to cite

APA:

Bereyhi, A., & Müller, R. (2018). Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Alberta, CA: IEEE.

MLA:

Bereyhi, Ali, and Ralf Müller. "Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing." Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Alberta IEEE, 2018.

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