Agreed.
Gaël
<div>-------- Original message --------</div><div>From: Sturla Molden
<sturla.mol...@gmail.com> </div><div>Date:18/02/2014 20:00 (GMT+01:00)
</div><div>To: scikit-learn-general@lists.sourceforge.net </div><div>Subject:
Re: [Scikit-learn-general] fit a GMM to a histogram (or weighted
data points) ? </div><div>
</div>Whether the initial data reduction is core sets or histograms, the
procedure for fitting a weighted GMM would still be the same.
Sturla
Charles Greenberg <great...@gmail.com> wrote:
> To give some more info, my application is fitting to processed data.
> Basically, it's too expensive to keep all individual data points so we
> keep a (3D) histogram, but we still want to fit a GMM to the data.
> Additionally, we perform processing on the histogram, averaging from
> multiple experiments. My current solution (re-sampling from the
> histogram) does work but 1) it's kind of annoying and 2) I have to sample
> a LOT of points so it slows things down.
>
> --Charles
>
> On Sun, Feb 16, 2014 at 11:02 AM, Sturla Molden
> <sturla.mol...@gmail.com>wrote:
>
> On 16/02/14 19:47, Gael Varoquaux wrote:
> On Sun, Feb 16, 2014 at 07:45:18PM +0100, Sturla Molden wrote:
> The main use case is to use the histogram as a form of down-sampling.
>
> In this case, I'd love to see core sets, which would be a more optimal
> way of down sampling. And actually, that answers my question above, I
> think that core sets would be a usecase for weighted samples.
>
> Yes, core-sets are possible too
>
> <a
> href="http://people.csail.mit.edu/dannyf/nips11.pdf";>http://people.csail.mit.edu/dannyf/nips11.pdf</a>
>
> Sturla
>
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