[SciPy-dev] Scikit for manifold learning techniques

[Matthieu Brucher]

Three voices in favor of the scikit, no voice against.
Other opinions ? I'd like to call it manifold_learning (obviously learn is
not a good option).
I think that the goal of learn is somewhat different that this scikit :
- learn is more about classification for the moment
- usually, a manifold learning technique is used before the classification
(and so the two scikits could be complementary)

If you read this, David, can you give an opinion on this ?

Matthieu

2007/12/6, Fernando Perez <fperez.net at gmail.com>:
>
> On Dec 5, 2007 10:04 AM, Matthieu Brucher <matthieu.brucher at gmail.com>
> wrote:
> > Hi,
> >
> > I'd like to create a new scikit (I know I didn't put much effort in the
> > optimizers, but it will change when I will have more time) for manifold
> > learning. At first, I'd like to implement some usual techniques like
> Isomap,
> > LLE (some are in neuroimaging I heard) with different levels of
> interaction.
> > I do this in my PhD thesis, so it is almost available like a scikit. It
> > would be a twin-like of the Dimensionality Reduction toolbox for MatLab
> but
> > with a different interaction : directly call the right global function
> (like
> > isomap, mds, nlm or gedodesicNLM ATM) or give directly to an optimizer
> the
> > cost function you want with a distance matrix (it will use my own
> > optimizers).
> > Eigenmaps will be available shortly (I have a referee that want it, so I
> > will implement it), it will use scipy.sparse, and I hope I'll be able to
> > propose two interfaces as well.
> >
> > If everything goes smoothly, I'll propose my own dimensionality
> reduction
> > technique in the scikit as well.
> >
> > Comments ?
>
> Another enthusiastic +1 from the peanut gallery!
>
> Cheers,
>
> f
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-- 
French PhD student
Website : http://miles.developpez.com/
Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92
LinkedIn : http://www.linkedin.com/in/matthieubrucher
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