Mercurial > hg > smallbox
changeset 213:46b9937982f6 luisf_dev
merge
author | bmailhe |
---|---|
date | Wed, 21 Mar 2012 18:13:43 +0000 |
parents | 1d134a1b6f95 (current diff) 0c7c20f3246c (diff) |
children | f0552f962930 |
files | |
diffstat | 2 files changed, 33 insertions(+), 1 deletions(-) [+] |
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--- a/DL/Majorization Minimization DL/wrapper_mm_DL.m Wed Mar 21 18:13:02 2012 +0000 +++ b/DL/Majorization Minimization DL/wrapper_mm_DL.m Wed Mar 21 18:13:43 2012 +0000 @@ -1,4 +1,36 @@ function DL = wrapper_mm_DL(Problem, DL) +%% SMALL wrapper for Majorization Minimization Dictionary Learning Algorithm +% +% Function gets as input Problem and Dictionary Learning (DL) structures +% and outputs the learned Dictionary. + +% In Problem structure field b with the training set needs to be defined. + +% In DL fields with name of the Dictionary update method and parameters +% for particular dictionary learning technique need to be present. For +% the orignal version of MM algorithm the update method should be: +% - 'mm_cn' - Regularized DL with column norm contraint +% - 'mm_fn' - Regularized DL with Frobenius norm contraint +% Alternatively, for comparison purposes the following Dictioanry update +% methods (which do not represent the optimised version of the algorithm) +% be used: +% - 'mod_cn' - Method of Optimized Direction +% - 'map-cn' - Maximum a Posteriory Dictionary update +% - 'ksvd-cn'- KSVD update +% +% - MM-DL - Yaghoobi, M.; Blumensath, T,; Davies M.; , "Dictionary +% Learning for Sparse Approximation with Majorization Method," IEEE +% Transactions on Signal Processing, vol.57, no.6, pp.2178-2191, 2009. + +% Centre for Digital Music, Queen Mary, University of London. +% This file copyright 2011 Ivan Damnjanovic. +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as +% published by the Free Software Foundation; either version 2 of the +% License, or (at your option) any later version. See the file +% COPYING included with this distribution for more information. +%% % determine which solver is used for sparse representation %
--- a/DL/Majorization Minimization DL/wrapper_mm_solver.m Wed Mar 21 18:13:02 2012 +0000 +++ b/DL/Majorization Minimization DL/wrapper_mm_solver.m Wed Mar 21 18:13:43 2012 +0000 @@ -1,5 +1,5 @@ function [X , cost] = wrapper_mm_solver(b, A, param) -%% SMALL wrapper for Majorization Maximization toolbos solver +%% SMALL wrapper for Majorization Minimization toolbox solver % % Function gets as input % b - measurement vector