2009/6/9 Robin <robi...@gmail.com>: > On Mon, Jun 8, 2009 at 7:14 PM, David Warde-Farley<d...@cs.toronto.edu> wrote: >> >> 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. :) > > Hi, > > I've been following this with interest... although I'm not really > familiar with the area. At the risk of drifting further off topic I > wondered if anyone could recommend an accessible review of these kind > of dimensionality reduction techniques... I am familiar with PCA and > know of diffusion maps and ICA and others, but I'd never heard of EM > and I don't really have any idea how they relate to each other and > which might be better for one job or the other... so some sort of > primer would be really handy.
Hi, Check Ch. Bishop publication on Probabilistic Principal Components Analysis, you have there the parallel between the two (EM is in fact just a way of computing PPCA, and with some Gaussian assumptions, you get PCA). Matthieu -- Information System Engineer, Ph.D. Website: http://matthieu-brucher.developpez.com/ Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn: http://www.linkedin.com/in/matthieubrucher _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
