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

Integration of Majorization Minimisation Dictionary Learning

+function [] = DLMM_Audio(m,n,l,it,Lambda,admiss)
+M = str2num(m);
+N = str2num(n);
+L = str2num(l);
+IT = str2num(it);
+lambda = str2num(Lambda);
+load(['Param',num2str(M),'X',num2str(N),'X',num2str(L),'kADCT2X01.mat'])
+method = ['bn';'un'];
+res = 1;
+while method(res)~=admiss
+    res = res+1;
+end
+[PhiN,PhiM] = size(Phio);
+Phi = randn(M,N);
+Phi = Phi*diag(1./sqrt(sum(Phi.^2)));
+[PhiN,L] = size(x);
+phim = PhiM;
+unhat = zeros(PhiM,L);
+maxIST = 10;
+maxIDU = 10;
+it = 1;
+ert(1) = norm(Phi*unhat-x,'fro')^2 + lambda*(sum(sum(abs(unhat))));
+while it <= IT
+    it
+%     %%%%%% Iterative Soft-Thresholding for Sparse Approximation %%%%%
+    tau2 = .1+(svds(Phi,1))^2;   
+    eps = 3*10^-4;
+    map = 0; % Projecting on the selected space (0=no,1=yes)
+    [unhat,er] = mm1(Phi,x,unhat,tau2,lambda,maxIST,eps,map); 
+    er
+    %%%%%% Majorisation Minimisation for Dictionary Update %%%%%%    
+    c = .1 + svds(unhat,1)^2; 
+    eps = 10^-7;
+    [Phi,unhat] = dict_update_REG_cn(Phi,x,unhat,maxIDU,eps,'un');
+    %%%%%
+    ert(it+1) = norm(Phi*unhat-x,'fro')^2 + lambda*(sum(sum(abs(unhat))));    
+    it = it+1;        
+end
+save(['DLMMl1',num2str(PhiN),'X',num2str(PhiM),'X',num2str(L),'iK',method(res,:),'Lambda',num2str(lambda),'Audio.mat'],'Phi','x','unhat','ert','lambda')