On Mon, Jun 8, 2009 at 3:29 AM, Gael Varoquaux <gael.varoqu...@normalesup.org> wrote: > On Mon, Jun 08, 2009 at 08:58:29AM +0200, Matthieu Brucher wrote: >> Given the number of PCs, I think you may just be measuring noise. >> As said in several manifold reduction publications (as the ones by >> Torbjorn Vik who published on robust PCA for medical imaging), you >> cannot expect to have more than 4 or 5 meaningful PCs, due to the >> dimensionality curse. If you want 50 PCs, you have to have at least... >> 10^50 samples, which is quite a lot, let's say it this way. >> According to the litterature, a usual manifold can be described by 4 >> or 5 variables. If you have more, it is that you may be infringing >> your hypothesis, here the linearity of your data (and as it is medical >> imaging, you know from the beginning that this hypothesis is wrong). >> So if you really want to find something meaningful and/or physical, >> you should use a real dimensionality reduction, preferably a >> non-linear one. > > I am not sure I am following you: I have time-varying signals. I am not > taking a shot of the same process over and over again. My intuition tells > me that I have more than 5 meaningful patterns. > > Anyhow, I do some more analysis behind that (ICA actually), and I do find > more than 5 patterns of interest that I not noise.
Just curious: whats the actual shape of the array/data you run your PCA on. Number of time periods, size of cross section at point in time? Josef _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
