[Numpy-discussion] performance matrix multiplication vs. matlab

Matthieu Brucher matthieu.brucher at gmail.com
Tue Jun 9 03:57:18 EDT 2009

2009/6/9 Robin <robince at gmail.com>:
> 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. :)
> 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.


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).

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