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

Integration of Majorization Minimisation Dictionary Learning

+%%% MOD (||ki||<=1) %%%%
+function mod_exactRec(it,k,sn)
+tic
+IT = str2num(it);
+K = str2num(k);
+SN = str2num(sn);
+if SN<10,
+    samnum = ['0',num2str(SN)];
+else
+    samnum = num2str(SN);
+end
+load(['Param',num2str(K),'kS',samnum,'.mat'])
+method = ['bn';'un'];
+res = 2; % 1 for bounded-norm, 2 for unit-norm
+lambda = 2*.2; % 2 * Smallest coefficients (Soft Thresholding)
+% lambda = 2*.2^2; % 2 * Smallest coefficients (Hard Thresholding)
+%%%%%%%%%%%%%%
+Phi = Phio;
+[PhiN,PhiM] = size(Phi);
+RR1 = PhiM;
+%%%%%%%%%%%%%%
+[PhiM,L] = size(ud);
+unhat = ones(PhiM,L);
+for it = 1:IT
+    it
+    to = .1+svds(Phi,1);
+    [PhiN,PhiM] = size(Phi);
+    %%%%
+%     eps = 10^-7;
+    eps = 3*10^-4;
+    maxIT = 1000;
+    map = 0;
+    [unhat,l1err] = mm1(Phi,x,unhat,to,lambda,maxIT,eps,map); %% Sparse approximation with Iterative Soft-thresholding
+    ert(it) = l1err;
+    %%%   
+    [Phi,unhat] = modcn(x,unhat,res);
+end
+save(['MODl120t',num2str(IT),'iki',method(res,:),num2str(K),'v2d',num2str(SN),'.mat'],'Phi','Phid','x','ud','unhat','ert')
+toc