[Numpy-discussion] performance matrix multiplication vs. matlab

[Jason Rennie]

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 <
david at 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
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-- 
Jason Rennie
Research Scientist, ITA Software
617-714-2645
http://www.itasoftware.com/
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