Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm.
IEEE International Conference on Acoustics, Speech and SP(2015)
摘要
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.
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关键词
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
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