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 method to calculate the percentage explained by the first principal component? Thanks for your advices on this. Fred ______________________________________________ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
