comparison DL/Majorization Minimization DL/wrapper_mm_DL.m @ 211:0c7c20f3246c luisf_dev

Added comments to ~/DL/Majorization Minimization DL/wrapper_mm_DL.m and ~/DL/Majorization Minimization DL/wrapper_mm_solver.m files.

 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 %
 
 solver = DL.param.solver;