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

Integration of Majorization Minimisation Dictionary Learning

+% MOD method for Dictionary Learning
+% Y = input data (M X L matrix)
+% Phi = initial dictionary (M X N), e.g. random dictionary or first N data samples
+% lambda = regularization coefficient (||Phi*X-Y||_F)^2 + lambda*||X||_1
+% IT = number of iterations
+% res = dictionary constraint. 'un' = unit colomn norm, 'bn' = bounded colomn norm
+function [Phiout,X,ert] = mod_cn(Y,Phi,lambda,IT)
+maxIT = 1000;
+[PhiN,PhiM] = size(Phi);
+RR1 = PhiM;
+%%%%%%%%%%%%%%
+[PhiN,L] = size(Y);
+X = ones(PhiM,L);
+for it = 1:IT
+    to = .1+svds(Phi,1);
+    [PhiN,PhiM] = size(Phi);
+    %%%%
+    eps = 3*10^-4;    
+    map = 1; % Projecting on the selected space (0=no,1=yes)
+    [X,l1err] = mm1(Phi,Y,X,to,lambda,maxIT,eps,map); %% Sparse approximation with Iterative Soft-thresholding
+    ert(it) = l1err;
+    %%%       
+    res = 2; % Fixed column norm dictionary constraint.
+    [Phi,X] = dict_update_MOD_cn(Y,X,res);  
+end
+Phiout = Phi;