[SciPy-dev] Scikit for manifold learning techniques

[Zachary Pincus]

> 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.

Oh, this would be most fantastic. If desired, I can donate a PCA  
implementation, which would be a good "baseline" method to have in a  
dimensionality-reduction kit.

Zach

(PS. Yes, PCA is easy to implement, but it is also easy to get subtly  
wrong -- I've seen several such -- or to implement in a way that is a  
lot slower than it needs to be. I've spent a while making my  
implementation correct and as fast as possible for both n_data >>  
n_dims and vice-versa. If anyone wants, I'll send the code.)