comparison util/classes/dictionaryMatrices/grassmannian.m @ 170:68fb71aa5339 danieleb

Added dictionary decorrelation functions and test script for Letters paper.

 %% Parameters and Defaults
 error(nargchk(2,7,nargin));
 
 if ~exist('verb','var')  || isempty(verb),  verb = false; end %verbose output
 if ~exist('initA','var') || isempty(initA), initA = randn(n,m); end %initial matrix
-if ~exist('dd2','var')   || isempty(dd2),   dd2 = 0.99; end %shrinking factor
+if ~exist('dd2','var')   || isempty(dd2),   dd2 = 0.9; end %shrinking factor
 if ~exist('dd1','var')   || isempty(dd1),   dd1 = 0.9; end %percentage of coherences to be shrinked
 if ~exist('nIter','var') || isempty(nIter), nIter = 10; end %number of iterations
 
 %% Main algo
 A = normc(initA); %normalise columns

 		Geye = G - eye(size(G));
 		fprintf(1,'%6i    %12.8f  %12.8f  \n',iIter,muMin,max(abs(Geye(:))));
 	end
 end
 
-% [~, Sigma_gram V_gram] = svd(G); %calculate svd decomposition of gramian
-
-% A = normc(A);					%normalise dictionary
-
 [V_gram Sigma_gram] = svd(G);				%calculate svd decomposition of gramian
 Sigma_new = sqrt(Sigma_gram(1:n,:)).*sign(Sigma); %calculate singular values of dictionary
 A = Uinit*Sigma_new*V_gram';				%update dictionary
-
-% param.step  = 0.01;
-% param.reg   = 0.01;
-% param.nIter = 20;
-% A = rotatematrix(initA,A,'linesearchlie',param);
-
-% %% Debug visualization function
-% function plotcart2d(A)
-% compass(A(1,:),A(2,:));