comparison toolboxes/FullBNT-1.0.7/KPMtools/pca_kpm.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [pc_vec]=pca_kpm(features,N, method);
+% PCA_KPM Compute top N principal components using eigs or svd.
+% [pc_vec]=pca_kpm(features,N) 
+%
+% features(:,i) is the i'th example - each COLUMN is an observation
+% pc_vec(:,j) is the j'th basis function onto which you should project the data
+% using pc_vec' * features
+
+[d ncases] = size(features);
+fm=features-repmat(mean(features,2), 1, ncases);
+
+
+if method==1 % d*d < d*ncases
+  fprintf('pca_kpm eigs\n');
+  options.disp = 0;
+  C = cov(fm'); % d x d matrix
+  [pc_vec, evals] = eigs(C, N, 'LM', options);
+else 
+  % [U,D,V] = SVD(fm), U(:,i)=evec of fm fm', V(:,i) = evec of fm' fm
+  fprintf('pca_kpm svds\n');
+  [U,D,V] = svds(fm', N);
+  pc_vec = V;
+end
+
+if 0
+X = randn(5,3);
+X = X-repmat(mean(X),5,1);
+C = X'*X;
+C2=cov(X)
+[U,D,V]=svd(X);
+[V2,D2]=eig(X)
+end