comparison DL/Majorization Minimization DL/ExactDicoRecovery/ksvd_cn.m @ 159:23763c5fbda5 danieleb

Merge

+% K-SVD algorithm 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
+function [Phiout,X,ert] = ksvd_cn(Y,Phi,lambda,IT)
+maxIT = 1000;
+[PhiN,PhiM] = size(Phi);
+RR1 = PhiM;
+%%%%%%%%%%%%%%
+% [PhiM,L] = size(ud);
+[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;
+    %%%          
+    [Phi,X] = dict_update_KSVD_cn(Phi,Y,X);  
+end
+Phiout = Phi;