Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm

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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:arXiv.org
Hlavní autori: Vila, Jeremy, Schniter, Philip, Rangan, Sundeep, Krzakala, Florent, Zdeborova, Lenka
Médium: Paper
Jazyk:English
Vydavateľské údaje: Ithaca Cornell University Library, arXiv.org 05.12.2014
Predmet:
Algorithms
Conditioning
Damping
Divergence
Message passing
Robustness (mathematics)
ISSN:2331-8422
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 \(A\).
Bibliografia:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.1412.2005