Mercurial > hg > smallbox
view DL/Majorization Minimization DL/ExactDicoRecovery/mapdl_cn.m @ 219:4337e28183f1 luisf_dev
Modified help comments of wrapper_mm_DL.m, wrapper_mm_solver.m, SMALL_rlsdla.m & SMALL_AudioDenoise_DL_test_KSVDvsSPAMS.m. Moved wrapper_ALPS_toolbox from toolboxes to toolboxes/alps and added some extra help comments.
author | Aris Gretsistas <aris.gretsistas@elec.qmul.ac.uk> |
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date | Fri, 23 Mar 2012 20:48:25 +0000 |
parents | b14209313ba4 |
children |
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% Maximum A Posteriori Estimation 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] = mapdl_cn(Y,Phi,lambda,IT,res) 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; %%% eps = 10^-7; mu = 10^-4; [Phi,X] = dict_update_MAP_cn(Phi,Y,X,mu,maxIT,eps,res); end Phiout = Phi;