Mercurial > hg > smallbox
view DL/Majorization Minimization DL/mm1.m @ 189:75b5dedcfd45 luisf_dev
created initialization file; changing SMALL_learn in order to initialize plugins.
author | luisf <luis.figueira@eecs.qmul.ac.uk> |
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date | Thu, 16 Feb 2012 18:24:43 +0000 |
parents | b14209313ba4 |
children |
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function [unhat,er] = mm1(Phi,x,u0,to,lambda,maxIT,eps,map) %% Iterative Soft Thresholding (with optional debiasing) % % Phi = Normalized Dictionary % x = Signal(x). This can be a vector or a matrix % u0 = Initial guess for the coefficients % to = 1/(step size) . It is larger than spectral norm of dictionary Phi % lambda = Lagrangian multiplier. (regulates shrinkage) % eps = Stopping criterion for iterative softthresholding and MM dictionary update % map = Debiasing. 0 = No, 1 = Yes % unhat = Updated coefficients % er = Objective cost %% cont = 1; in = 1; % un = zeros(size(u0,1),size(u0,2)); un = u0; c1 = (1/to^2)*Phi'*x; c2 = (1/to^2)*(Phi'*Phi); %%%% while (cont && (in<=maxIT)) unold = un; %%%%%% Soft Thresholding %%%%%%% alphap = (un + c1 - c2*un); un = (alphap-(lambda/(2*to^2))*sign(alphap)).*(abs(alphap)>=(lambda/(2*to^2))); in = in+1; cont = sum(sum((unold-un).^2))>eps; end %%%%%%%%%% if map == 1, %% Mapping on the selected space %%%% [uN,uM] = size(un); unhat = zeros(uN,uM); for l = 1:uM, unz = (abs(un(:,l))>0); M = diag(unz); PhiNew = Phi*M; PhiS = PhiNew(:,unz); unt = inv(PhiS'*PhiS+.0001*eye(sum(unz)))*PhiS'*x(:,l); unhat(unz,l) = unt; end else unhat = un; end %%% Cost function calculation if map == 1, er = sum(sum((Phi*unhat-x).^2))+lambda*(sum(sum(abs(unhat)>0))); %% l_0 Cost function else er = sum(sum((Phi*unhat-x).^2))+lambda*(sum(sum(abs(unhat)))); end