Name
Title | ||
|---|---|---|
Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm. |
Abstract | ||
|---|---|---|
The generalized approximate message passing (GAMP) algorithm is an efficient method of MAP or approximate-MMSE estimation of x observed from a noisy version of the transform coefficients z = Ax. In fact, for large zero-mean i.i.d sub-Gaussian A, GAMP is characterized by a state evolution whose fixed points, when unique, are optimal. For generic A, however, GAMP may diverge. In this paper, we propose adaptive-damping and mean-removal strategies that aim to prevent divergence. Numerical results demonstrate significantly enhanced robustness to non-zero-mean, rank-deficient, column-correlated, and ill-conditioned A. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2015.7178325 | IEEE International Conference on Acoustics, Speech and SP |
Keywords | Field | DocType |
Gaussian distribution,least mean squares methods,message passing,GAMP algorithm,MAP estimation,adaptive damping,approximate-MMSE estimation,column-correlated A,generalized approximate message passing algorithm,ill-conditioned A,mean removal,nonzero-mean A,rank-deficient A,state evolution,transform coefficients,zero-mean i.i.d sub-Gaussian A,Approximate message passing,belief propagation,compressed sensing | Mathematical optimization,State evolution,Divergence,Computer science,Algorithm,Robustness (computer science),Fixed point,Additive white Gaussian noise,Compressed sensing,Message passing,Belief propagation | Journal |
Volume | ISSN | Citations |
abs/1412.2005 | Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE
International Conference on Year: 2015 Pages: 2021 - 2025 | 34 |
PageRank | References | Authors |
1.13 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy P. Vila | 1 | 106 | 4.38 |
| Philip Schniter | 2 | 1620 | 93.74 |
| Sundeep Rangan | 3 | 35 | 1.49 |
| Florent Krzakala | 4 | 977 | 67.30 |
| Lenka Zdeborová | 5 | 1190 | 78.62 |