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

Integration of Majorization Minimisation Dictionary Learning

+clear
+IT = 1000; % Number of iterations
+R = 3:7; % Sparsity of the signals
+SN = 5; % Number of trials
+M = 20; % Signal space dimension
+N = 40; % Ambient space dimension
+L = 32*N; % Number of training signals
+
+ParamGen(M,N,L,R,SN) % Generating generic problems
+
+%%%% Dictionary recovery algorithms
+for sn = 1:SN    
+    for r = R
+        mmdlcn_exactRec_demo(IT,r,sn,'un')
+        mapcn_exactRec_demo(IT,r,sn,'bn')
+        ksvd_exactRec_demo(IT,r,sn)
+        modcn_exactRec_demo(IT,r,sn)
+    end
+end
+%%%%%%%
+Tre = .99;
+for k = R
+    for i=1:SN
+        load(['RDLl1',num2str(M),'t',num2str(IT),'ikiun',num2str(k),'v1d',num2str(i),'.mat'])
+        nrRDLu(i,k-R(1)+1) = sum(max(abs((Phid'*Phi)))>=Tre);
+        %%%%%%
+        load(['MAPl1',num2str(M),'t',num2str(IT),'ikiun',num2str(k),'v1d',num2str(i),'.mat'])
+        nrMAP(i,k-R(1)+1) = sum(max(abs((Phid'*Phi)))>=Tre);        
+        %%%%%%
+        load(['KSVDl1',num2str(M),'t',num2str(IT),'ikiun',num2str(k),'v1d',num2str(i),'.mat'])
+        nrKSVD(i,k-R(1)+1) = sum(max(abs((Phid'*Phi)))>=Tre);
+        %%%%%%
+        load(['MODl1',num2str(M),'t',num2str(IT),'ikiun',num2str(k),'v1d',num2str(i),'.mat'])
+        nrMOD(i,k-R(1)+1) = sum(max(abs((Phid'*Phi)))>=Tre);
+    end
+end
+clf
+errorbar(R+.01,10*mean(nrRDLu,1)/4,std(nrRDLu,0,1),'k-.')
+hold on
+errorbar(R-.01,10*mean(nrKSVD,1)/4,std(nrKSVD,0,1),'r*-')
+errorbar(R+.01,10*mean(nrMOD,1)/4,std(nrMOD,0,1),'b-v')
+errorbar(R-.01,10*mean(nrMAP,1)/4,std(nrMAP,0,1),'b-^')
+axis([2.5 7.5 15 105]);
+title('Constrained column-norm dictionaries')
+xlabel('Sparsity (# of non-zero elements in each coefficient vector)')
+ylabel(['Average percents of exact recovery',sprintf('\n'),'after ',num2str(IT),' iterations'])
+grid on
+legend('MM Unit norm','K-SVD','MOD','MAPbased-DL');
+axis([2.8 7.2 -5 105])