There was a discussion
<http://www.mail-archive.com/scikit-learn-general@lists.sourceforge.net/msg06931.html>
on metric learning a while ago, and several people expressed interest to
see (and contribute to) it in sklearn. But, it looks like that attempt
didn't get anywhere.
What about a project to add several metric learning algorithms to be used
with KNN (NCA, ITML, LMNN, etc). Another application is data
transformation: most of the methods learn some PSD matrix A, so we can
transform the data by multiplying it by A^{-1/2}.
The starting point could be an ICML tutorial
<http://www.slideshare.net/zukun/metric-learning-icml2010-tutorial> by
Brian Kulis with a field overview.
On Tue, Feb 24, 2015 at 5:48 PM, Andy <t3k...@gmail.com> wrote:
> Hey Everybody.
>
> Here is my somewhat consolidated list of ideas with minor comments.
> If anything is missing, please let me know. Also, I don't think people
> who want to mentor spoke up yet.
> I'll remove all people listed on the wiki as they were copy and pasted
> from last year, and I'd rather have actual confirmation.
>
> Topics:
> DPGMM / VBGMM: need to be reimplemented using more standard variational
> updates. The GMM is actually fine atm (after a couple of pending PRs)
>
> spearmint : Using random forest (they actually use ours) for
> hyperparameter optimization. I need to mull this over but I think this
> should be easy enough and pretty helpful.
>
> Online low-rank matrix completion : this is from last year and I'm not
> sure if it is still desirable / don't know the state of the PR
>
> Multiple metric support : This is somewhat API heavy but I think
>
> PLS/CCA : They need love so very much, but I'm not sure we have a mentor
> (if there is one, please speak up!):q
>
> Ensemble Clusters : Proposed by a possible student (Milton) but I think
> it is interesting.
>
> Semi-Supervised Learning : Meta-estimator for self-taught learning. Not
> sure if there is actually much demand for it, but would be nice.
>
> Additive models: Proposed by ragv, but I'm actually not that sold. We
> could include pyearth, but I'm not sure how valuable the other methods
> are. Including a significant amount of algorithms just for completeness
> is not something I feel great about.
>
>
> That being said, ragv has put in a tremendous amount of great work and I
> feel we should definitely find a project for him (as he seems interested).
>
>
> Things that I think shouldn't be GSOC projects:
>
> GPs : Jan Hendrik is doing an awesome job there.
> MLP : Will be finished soon, either by me or possibly by ragv
> data-independent cross-validation : already a bunch of people working on
> that, I don't think we should make it GSOC.
>
> Feedback welcome.
>
> Andy
>
>
>
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