Paper Info
Title
A fast algorithm based on a sylvester-like equation for LS regression with GMRF prior
Abstract
This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior. More precisely, the computation of the proximity operator of the LS criterion regularized by different GMRF potentials is formulated as solving a Sylvester-like matrix equation. By exploiting the structural properties of GMRFs, this matrix equation is solved column-wise in an analytical way. The proposed algorithm can be embedded into a wide range of proximal algorithms to solve LS regression problems including a convex penalty. Experiments carried out in the case of a constrained LS regression problem arising in a multichannel image processing application, provide evidence that an alternating direction method of multipliers performs quite efficiently in this context.
Year
DOI
Venue
2017
10.1109/CAMSAP.2017.8313057
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Keywords
DocType
Volume
multichannel image processing application,constrained LS regression problem,proximal algorithms,matrix equation,LS criterion,2D Gaussian Markov random field,penalized least squares regression problems,GMRF prior,Sylvester-like equation
Conference
abs/1709.06178
ISBN
Citations 
PageRank 
978-1-5386-1252-1
0
0.34
References 
Authors
18
4
Name
Order
Citations
PageRank
Qi Wei126513.31
Emilie Chouzenoux220226.37
Jean-Yves Tourneret31154104.46
Jean-Christophe Pesquet41811.52