On 8-Jun-09, at 8:33 AM, Jason Rennie wrote: Note that EM can be very slow to converge: That's absolutely true, but EM for PCA can be a life saver in cases where diagonalizing (or even computing) the full covariance matrix is not a realistic option. Diagonalization can be a lot of wasted effort if all you care about are a few leading eigenvectors. EM also lets you deal with missing values in a principled way, which I don't think you can do with standard SVD. EM certainly isn't a magic bullet but there are circumstances where it's appropriate. I'm a big fan of the ECG paper too. :) David |
_______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
