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 <[email protected]>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
>
> http://people.csail.mit.edu/dannyf/nips11.pdf
>
>
>
> Sturla
>
>
>
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