2009/6/8 David Warde-Farley <d...@cs.toronto.edu>: > > On 8-Jun-09, at 1:17 AM, David Cournapeau wrote: > >> I would not be surprised if David had this paper in mind :) >> >> http://www.cs.toronto.edu/~roweis/papers/empca.pdf > > Right you are :) > > There is a slight trick to it, though, in that it won't produce an > orthogonal basis on its own, just something that spans that principal > subspace. So you typically have to at least extract the first PC > independently to uniquely orient your basis. You can then either > subtract off the projection of the data on the 1st PC and find the > next one, one at at time, or extract a spanning set all at once and > orthogonalize with respect to the first PC. > > David
Also Ch. Bishop has an article on using EM for PCA, Probabilistic Principal Components Analysis where I think he proves the equivalence as well. 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
