shogun-toolbox/shogun — pull request #410 by iglesias (2012-04-01 21:59:59+00:00)
Title: Stochastic Proximity Embedding (SPE)

Here it goes an implementation of this method for dimensionality reduction (DR). I have used the code in the MATLAB toolbox for DR as a reference.

Within SPE there are two different strategies, local, which uses kNN, and global that can be used.

I have also included some examples:

- One in C++ that I have used in conjunction with valgrind to check that there were no memory leaks in the implementation.
- One for python_modular with basic use.
- One graphical for python_modular for helix input data. Please find here how the result should look like: http://dl.dropbox.com/u/11020840/shogun/spes_iso_helix_r%26g.png The result with local SPE may seem pretty poor but it is actually the same that the MATLAB toolbox produces. The result with global SPE matches as well the one in the toolbox.

Please let me know about flaws and inconsistencies you find in the implementation. I am sure there must be things that really do not fit with the other DR techniques.

Looking forward for hearing your feedback :)