Note that EM can be very slow to converge: http://www.cs.toronto.edu/~roweis/papers/emecgicml03.pdf
EM is great for churning-out papers, not so great for getting real work done. Conjugate gradient is a much better tool, at least in my (and Salakhutdinov's) experience. Btw, have you considered how much the Gaussianity assumption is hurting you? Jason On Mon, Jun 8, 2009 at 1:17 AM, David Cournapeau < da...@ar.media.kyoto-u.ac.jp> wrote: > Gael Varoquaux wrote: > > I am using the heuristic exposed in > > http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4562996 > > > > We have very noisy and long time series. My experience is that most > > model-based heuristics for choosing the number of PCs retained give us > > way too much on this problem (they simply keep diverging if I add noise > > at the end of the time series). The algorithm we use gives us ~50 > > interesting PCs (each composed of 50 000 dimensions). That happens to be > > quite right based on our experience with the signal. However, being > > fairly new to statistics, I am not aware of the EM algorithm that you > > mention. I'd be interested in a reference, to see if I can use that > > algorithm. > > I would not be surprised if David had this paper in mind :) > > http://www.cs.toronto.edu/~roweis/papers/empca.pdf<http://www.cs.toronto.edu/%7Eroweis/papers/empca.pdf> > > cheers, > > David > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Jason Rennie Research Scientist, ITA Software 617-714-2645 http://www.itasoftware.com/
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