On Sat, Apr 6, 2013 at 5:00 PM, Andreas Mueller wrote:
> Hi Vladan and welcom to sklearn :)
>
Hi Andy,
thanks for your fast reply and greeting :)
I think what you describe is some particular transductive setting in which
> you have training labels for some classes, but not all.
> Transductive means that you know before-hand which data you want to
> predict on (i.e. you can use all your data, have labels on
> some, and infer the labels on others)
> I don't think there is anything in scikit-learn that is particularly
> tailored to your situation.
> Do you know hot many labels there are in advance?
>
Not much, it should not be greater then 50 I guess. It's very similar to
musical classifications (for example genre), only that signals are not
music.
>
> The most simple solution that comes to my mind is just use a clustering
> mechanism on the whole data,
> than assign labels to clusters via the training labels you have - and if a
> cluster doesn't have enough labeled points, declare it a new
> label.
>
> If you want do do it "right", I would write down a generative model that
> says something about how classes come into existence and then do inference
> in that.
> For example, if each class is well modeled by a Gaussian, you could fit a
> GMM to your data where you enforce that samples that share a label
> belong to the same component.
>
> Hope that helps at least a bit.
>
It helps a lot, as now I know that there is no such special scenario in
sklearn.
I will try clustering, which I also would have done out of curiosity too,
and see if I can group already labeled data.
I'll read further about GMM suggestion
Thanks Andy :)
> Cheers,
> Andy
>
Cheers
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