2012/3/6 Mathieu Blondel <[email protected]>:
> Even if they would be useful, I'd rather avoid projects like
> "maintenance" or "speed things up". I think projects with a
> well-identified goal are more likely to be accepted by the PSF.
>
> I like Olivier's proposals for SGD-based low-rank and non-negative
> matrix factorization but I'd rather merge them into a single project.
> For the non-negative constraint, I believe we just need to add a
> clipping-to-zero step at each iteration. Vlad's idea of a Cython-based
> non-negative least squares solver is very good and would be a nice
> bonus if time permits.
>
> For students, I'd strongly recommend to submit a first pull-request to
> scikit-learn *before* you apply for the GSOC. Last year, by the time
> he applied for GSOC, Vlad had already merged his NMF code. This made
> his application very credible and convincing.

We could indeed merge both proposals in a single GSoC proposal, but I
would like to keep them as 2 separate steps with the two examples:
group lens movie recommendation and out-of-core NMF for topic modeling
on wikipedia text.

We could also work on making the MiniBatchSparseDirectionaryLearning
class work on sparse input (that might be a way to implement the
out-of-core NMF with positive clipping, it don't know if it's a good
idea or not from a theoretical point of view).

Is someone else (@mblondel) interested in mentoring (some of) this?

I think it would be good to have at least 3 different proposals with 3
different mentor names on the wiki page rather soon-ish to have
scikit-learn accepted as a member project of the PSF organization for
GSoC.

-- 
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel

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