annotate DL/Majorization Minimization DL/ExactDicoRecovery/mmdl_cn.m @ 224:fd0b5d36f6ad danieleb

Updated the contents of this branch with the contents of the default branch.

% Majorization Minimization 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] = mmdl_cn(Y,Phi,lambda,IT,res)
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;
    %%%
    eps = 10^-7;        
    [Phi,X] = dict_update_REG_cn(Phi,Y,X,maxIT,eps,res);  
end
Phiout = Phi;