> 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 distanc
This is also consistent with my experience.
-Robert
On Apr 22, 2013, at 5:56 PM, Kenneth C. Arnold wrote:
> 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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