Pages

Tuesday, September 01, 2015

Parametric Bilinear Generalized Approximate Message Passing - implementation -

Today, we have an extension of BiG-AMP, woohoo !

 


Parametric Bilinear Generalized Approximate Message Passing by Jason T. Parker, Yan Shou, Philip Schniter
We propose a scheme to estimate the parameters bi and cj of the bilinear form zm=i,jbiz(i,j)mcj from noisy measurements {ym}Mm=1, where ym and zm are related through an arbitrary likelihood function and z(i,j)m are known. Our scheme is based on generalized approximate message passing (G-AMP): it treats bi and cj as random variables and z(i,j)m as an i.i.d.\ Gaussian tensor in order to derive a tractable simplification of the sum-product algorithm in the large-system limit. It generalizes previous instances of bilinear G-AMP, such as those that estimate matrices B and C from a noisy measurement of Z=BC, allowing the application of AMP methods to problems such as self-calibration, blind deconvolution, and matrix compressive sensing. Numerical experiments confirm the accuracy and computational efficiency of the proposed approach.


The implementation can be found here: http://gampmatlab.wikia.com/wiki/Generalized_Approximate_Message_Passing 


Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments:

Post a Comment