On Mon, Jun 8, 2009 at 6:17 AM, Gael Varoquaux <gael.varoqu...@normalesup.org> wrote: > On Mon, Jun 08, 2009 at 09:02:12AM -0400, josef.p...@gmail.com wrote: >> whats the actual shape of the array/data you run your PCA on. > > 50 000 dimensions, 820 datapoints.
Have you tried shuffling each time series, performing PCA, looking at the magnitude of the largest eigenvalue, then repeating many times? That will give you an idea of how large the noise can be. Then you can see how many eigenvectors of the unshuffled data have eigenvalues greater than the noise. It would be kind of the empirical approach to random matrix theory. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
