For Mahalanobis metric, maybe we can do a cholesky decomposition to the
learned metric and make it a transformer? Then after it we can chain a knn
classifier after the transform.
Best,
Wei
On Tue, Apr 23, 2013 at 3:59 PM, Robert McGibbon <rmcgi...@gmail.com> wrote:
> 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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