comparison DL/Majorization Minimization DL/wrapper_mm_solver.m @ 155:b14209313ba4 ivand_dev

Integration of Majorization Minimisation Dictionary Learning

+function [X , cost] = wrapper_mm_solver(b, A, param)
+%% SMALL wrapper for Majorization Maximization toolbos solver
+%
+%   Function gets as input
+%       b - measurement vector 
+%       A - dictionary 
+%       param - structure containing additional parameters
+%   Output:
+%       x - sparse solution
+%       cost - Objective cost
+
+%   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.
+%   
+%%
+
+% Initial guess for the coefficients
+
+if (isfield(param, 'initcoeff'))
+   initX = param.initcoeff;
+else
+   initX = zeros(size(A,2),size(b,2));
+end
+
+% to - 1/(step size) . It is larger than spectral norm of dictionary A
+
+if isfield(param, 'to')
+   to = param.to;
+else
+   to = .1+svds(A,1);
+end
+
+% lambda - Lagrangian multiplier. (regulates shrinkage)
+
+if isfield(param, 'lambda')
+    lambda = param.lambda;
+else
+    lambda = 2*.2; 
+end
+
+% Inner-loop maximum iteration number.
+
+if isfield(param, 'iternum')
+    maxIT = param.iternum;
+else
+    maxIT = 1000; 
+end
+
+% Stopping criterion for iterative softthresholding
+
+if isfield(param, 'epsilon')
+    epsilon = param.epsilon;
+else
+    epsilon = 1e-7; 
+end
+
+% Debiasing. 0 = No, 1 = Yes
+
+if isfield(param, 'map')
+    map = param.map;
+else
+    map = 1; 
+end
+
+
+[X, cost] = mm1(A,b,initX,to,lambda,maxIT,epsilon,map); 
+cost
+end