Mercurial > hg > smallbox
view DL/Majorization Minimization DL/dict_update_REG_cn.m @ 197:d00e97b2f1cb luisf_dev
Fixed path reference to the current folder in SMALLBoxSetup.
author | luisf <luis.figueira@eecs.qmul.ac.uk> |
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date | Tue, 20 Mar 2012 10:40:59 +0000 |
parents | b14209313ba4 |
children |
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function [Phiout,unhatnz] = dict_update_REG_cn(Phi,x,unhat,maxIT,eps,cvset) %% Regularized Dictionary Learning with the constraint on the column norms %%%%% % Phi = Normalized Initial Dictionary % x = Signal(x). This can be a vector or a matrix % unhat = Initial guess for the coefficients % to = 1/(step size) . It is larger than spectral norm of coefficient matrix x % eps = Stopping criterion for iterative softthresholding and MM dictionary update % cvset = Dictionary constraint. 0 = Non convex ||d|| = 1, 1 = Convex ||d||<=1 % Phiout = Updated Dictionary % unhatnz Updated Coefficients (the same as input in this version) %% B = Phi; K = zeros(size(Phi,1),size(Phi,2)); c = .1 + svds(unhat,1)^2; %.1 c3 = (1/c)*eye(size(B,2)); c1 = x*unhat'*c3; c2 = (c*eye(size(B,2))-unhat*unhat')*c3; %% for i=1:maxIT % if i>1 % B = K; % end K = c1 + B*c2; if cvset == 1, K = K*diag(min(sum(K.^2).^(-.5),1)); % with convex constraint set else % Mehrdad original - % K = K*diag(sum(K.^2).^(-.5)); % with fixed-norm constraint set K = normc(K); end if (sum(sum((B-K).^2)) < eps) break; end B = K; end %% depleted atoms cancellation %%% [Y,I] = sort(sum(K.^2),'descend'); RR = sum(Y>=.01); Phiout = K(:,I(1:RR)); unhatnz = unhat(I(1:RR),:); end