[SciPy-user] Manifold Learning Technology Preview

Rob Clewley rob.clewley@gmail....
Mon Apr 7 10:28:58 CDT 2008


Matthieu,

I look forward to this! Are these going to be pure python
implementations only? I know that Isomap in Matlab came with a DLL for
faster processing of the networks -- is there any such plan to do this
in yours?

Best,
Rob


On Mon, Apr 7, 2008 at 9:48 AM, Matthieu Brucher
<matthieu.brucher@gmail.com> wrote:
> Hi,
>
> For those who want to use manifold learning tools, I'm happy to announce
> that scikits.learn has now an implementation of the usual techniques. They
> may not all work (I'm in the process of testing them and fixing the porting
> issues) at the moment, but they will in the near future.
>
> What's inside ?
>   - compression is where the usual techniques are located (PCA by Zachary
> Pincus, Isomap, LLE, Laplacian Eigenmaps, Hessian Eigenmaps, Diffusion maps,
> CCA and my own technique). Only the dimensionality reduction is done here,
> that is original space to a reduced space.
>    - regression is a set of multidimensional regression tools that will
> generate a model between the reduced space to the original space. Here is a
> linear model (called PCA, because it is generally used in conjunction with
> PCA) and a piecewise linear model
>    - projection will enable the projection on a new point on the manifold
> with the help of the model.
>
> No Nyström extension at the moment, but perhaps some one will create a
> regression model based on this.
> Some techniques create a reduced space and a model at the same time (with a
> fixed number of linear models, like Brandt's one), I did not implement them,
> but they could benefit from the projection module.
>
> I will add a tutorial on the scikits trac when I have some time, with
> details on the interfaces that can be used and reused.
>
> Here is a small test for people who want to test it right now. Suppose you
> have an array with 1000 points in a 3D space (so a 1000x3 array) :
>
> >>> from scikits.learn.machine.manifold_learning import compression
> >>> coords = compression.isomap(test, 2, neighbors=9)
>
> Here the Isomap algorithm was used, the test array was reduced from 3D to
> 2D, and the number of neighbors used to create the neighbors graph was 9 (in
> fact |point + number of neighbors| = 9, this may need some fixes).
>
> The TP does not need an additional scikit, only numpy and scipy (trunk) and
> optionally scikits.openopt (trunk) for CCA, my reduction technique and the
> projections (if needed).
>
> Matthieu
> --
> French PhD student
>  Website : http://matthieu-brucher.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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>
>

-- 
Robert H. Clewley, Ph. D.
Assistant Professor
Department of Mathematics and Statistics
Georgia State University
720 COE, 30 Pryor St
Atlanta, GA 30303, USA

tel: 404-413-6420 fax: 404-651-2246
http://www.mathstat.gsu.edu/~matrhc
http://brainsbehavior.gsu.edu/


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