[Numpy-discussion] performance matrix multiplication vs. matlab

Robin robince at gmail.com
Tue Jun 9 03:50:46 EDT 2009

On Mon, Jun 8, 2009 at 7:14 PM, David Warde-Farley<dwf at 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. :)


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.



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