Name
Abstract | ||
|---|---|---|
Generalized approximate message passing (GAMP) is an effective algorithm for recovering signals from noisy linear measurements, assuming known a priori signal distributions. However, in practice, both the signal distribution and noise level are often unknown. The EM-GM-AMP algorithm integrates GAMP with the EM algorithm to simultaneously estimate the signal distribution and noise variance while recovering the signal. EM-GM-AMP is built on the assumption that the signal is drawn from a sparse Gaussian mixture. In this paper, we propose nonparametric maximum likelihood-AMP (NPML-AMP) for estimating an arbitrary signal distribution in this setting. In addition to providing more flexibility (and performance improvements), we argue that the nonparametric approach actually simplifies implementation and improves stability by leveraging approximate convexity, which is not available in the sparse Gaussian mixture formulation of EM-GM-AMP. We also propose a simplified noise variance estimator for use in conjunction with NPML-AMP (or EM-GM-AMP). A comprehensive numerical study validates the performance of NPML-AMP algorithm in reaching nearly minimum mean squared error (MMSE) under various signal distributions, noise levels, and undersampling ratios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CISS.2017.7926084 | 2017 51st Annual Conference on Information Sciences and Systems (CISS) |
Keywords | Field | DocType |
nonparametric maximum likelihood approximate message passing,generalized approximate message passing,GAMP,signal recovery,noisy linear measurements,a priori signal distributions,noise level,EM-GM-AMP algorithm,noise variance,sparse Gaussian mixture,nonparametric maximum likelihood-AMP,NPML-AMP,noise variance estimator,minimum mean squared error,MMSE | Mathematical optimization,Computer science,Expectation–maximization algorithm,Undersampling,Minimum mean square error,Nonparametric statistics,Gaussian,Estimation theory,Maximum likelihood sequence estimation,Estimator | Conference |
ISBN | Citations | PageRank |
978-1-5090-2697-5 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Feng | 1 | 1 | 0.96 |
| Ruijun Ma | 2 | 5 | 2.78 |
| Lee H. Dicker | 3 | 3 | 3.02 |