Some gists:
https://gist.github.com/kcarnold/5439917
https://gist.github.com/kcarnold/5439945
They are rather terribly documented, sorry.
Input to such algorithms is usually given as:
- a set of similarity and dissimilarity links,
- relative comparisons (x is closer to y than w is to z), or
- target distances (x should be no farther than q from y).
The outputs of all methods I've worked with are Mahalanobis distances.
-Ken
On Mon, Apr 22, 2013 at 8:41 PM, Mathieu Blondel <math...@mblondel.org>wrote:
>
> On Mon, Apr 22, 2013 at 3:56 PM, Alexandre Gramfort <
> alexandre.gramf...@inria.fr> wrote:
>
>> > If people were interested in putting together a separate package in the
>> > style of the scikit collecting metric learning algorithms with a common
>> API,
>> > I would love to contribute to that too.
>>
>
> I'm also interested in metric learning but as others said we need to come
> up with a good API. For example, how would use expose the learned metric,
> say to reuse it in a k-NN? Since several people mentioned that they have
> code, it would be helpful to put it in a gist so we can come up with a
> unified API.
>
> Mathieu
>
>
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