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Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent = L(1)/sum(L); Others argue using Sigular Value Decomposition(SVD) to calculate the same quantity, as: [U,S,V]=svd(X); L = diag(S); L = L.^2; percent = L(1)/sum(L); So which way is the correct m...

I''d like to use ferret to build an imap indexer and search utility, but want to check first to see if anyone else is working on this and offer my help. Anyone? Also, if you could provide any helpful pointers on indexing directories via ferret, it''ll be very much appreciated. I''m a lucene nuby. Thanks! John -- Posted via http://www.ruby-forum.com/.