On Mon, Jun 08, 2009 at 06:28:06AM -0700, Keith Goodman wrote: > 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. Yes, that's the kind of things that is done in the paper I pointed out and I use to infer the number of PCAs I retain. Gaël _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
