>
> Are there any objections on Joel's variant of y? It serves my needs, but
> is quite different from what one can usually find in scikit-learn.
FWIW It'll require some changes to cross-validation routines.
On 22 March 2015 at 11:54, Artem <barmaley....@gmail.com> wrote:
> Are there any objections on Joel's variant of y? It serves my needs, but
> is quite different from what one can usually find in scikit-learn.
>
> ------
>
> Another point I want to bring up is metric-aware KMeans. Currently it
> works with Euclidean distance only, which is not a problem for a
> Mahalanobis distance, but as (and if) we move towards kernel metrics, it
> becomes impossible to transform the data in a way that the Euclidean
> distance between the transformed points accurately reflects the distance
> between the points in a space with the learned metric.
>
> I think it'd nice to have "non-linear" metrics, too. One of the possible
> approaches (widely recognized among researchers on metric learning) is to
> use KernelPCA before learning the metric. This would work really well with
> sklearn's Pipelines.
> But not all the methods are justified to be used with Kernel PCA. Namely,
> ITML uses a special kind of regularization that breaks all theoretical
> guarantees.
>
> And, it's a bit weird that something that is called a metric learning
> actually does space transformation. Maybe we should also add something like
> factories of metrics, whose sole result is a DistanceMetric (in particular
> for those kernel metrics)?
>
> On Fri, Mar 20, 2015 at 10:01 AM, Gael Varoquaux <
> gael.varoqu...@normalesup.org> wrote:
>
>> On Fri, Mar 20, 2015 at 11:50:37AM +1100, Zay Maung Maung Aye wrote:
>> > Neighborhood Component Analysis is more cited than ITML.
>>
>> There is already a pull request on neighborhood component analysis
>> https://github.com/scikit-learn/scikit-learn/issues/3213
>>
>> A first step of the GSoC could be to complete it.
>>
>> Gaël
>>
>> > On Wed, Mar 18, 2015 at 11:39 PM, Artem <barmaley....@gmail.com> wrote:
>>
>> > Hello everyone
>>
>> > Recently I mentioned metric learning as one of possible projects
>> for this
>> > years' GSoC, and would like to hear your comments.
>>
>> > Metric learning, as follows from the name, is about learning
>> distance
>> > functions. Usually the metric that is learned is a Mahalanobis
>> metric, thus
>> > the problem reduces to finding a PSD matrix A that minimizes some
>> > functional.
>>
>> > Metric learning is usually done in a supervised way, that is, a
>> user tells
>> > which points should be closer and which should be more distant. It
>> can be
>> > expressed either in form of "similar" / "dissimilar", or "A is
>> closer to B
>> > than to C".
>>
>> > Since metric learning is (mostly) about a PSD matrix A, one can
>> do Cholesky
>> > decomposition on it to obtain a matrix G to transform the data. It
>> could
>> > lead to something like guided clustering, where we first transform
>> the data
>> > space according to our prior knowledge of similarity.
>>
>> > Metric learning seems to be quite an active field of research ([1],
>> [2], [3
>> > ]). There are 2 somewhat up-to date surveys: [1] and [2].
>>
>> > Top 3 seemingly most cited methods (according to Google Scholar)
>> are
>>
>> > □ MMC by Xing et al. This is a pioneering work and, according to
>> the
>> > survey #2
>>
>> > The algorithm used to solve (1) is a simple projected
>> gradient
>> > approach requiring the full
>> >
>> > eigenvalue decomposition of
>> >
>> > M
>> >
>> > at each iteration. This is typically intractable for medium
>> >
>> > and high-dimensional problems
>>
>> > □ Large Margin Nearest Neighbor by Weinberger et al. The survey 2
>> > acknowledges this method as "one of the most widely-used
>> Mahalanobis
>> > distance learning methods"
>>
>> > LMNN generally performs very well in practice, although it
>> is
>> > sometimes prone to overfitting due to the absence of
>> > regularization, especially in high dimension
>>
>> > □ Information-theoretic metric learning by Davis et al. This one
>> features
>> > a special kind of regularizer called logDet.
>> > □ There are many other methods. If you guys know that other
>> methods rock,
>> > let me know.
>>
>> > So the project I'm proposing is about implementing 2nd or 3rd (or
>> both?)
>> > algorithms along with a relevant transformer.
>>
>> >
>>
>> ------------------------------------------------------------------------------
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>> > Scikit-learn-general mailing list
>> > Scikit-learn-general@lists.sourceforge.net
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>> --
>> Gael Varoquaux
>> Researcher, INRIA Parietal
>> Laboratoire de Neuro-Imagerie Assistee par Ordinateur
>> NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
>> Phone: ++ 33-1-69-08-79-68
>> http://gael-varoquaux.info
>> http://twitter.com/GaelVaroquaux
>>
>>
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>
>
>
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