[SciPy-Dev] Comparison of PLS algorithms

[Aman Thakral]

Hi all,

I've provided a summary of the features between the two PLS libraries
submitted.  Please see the table below:

    *Duchesnay* *Aman*  *Inner Loop* Both NIPALS and CCA NIPALS only  *Mean
Centering* Yes Yes  *Scaling to Unit Variance* Yes Yes  *SVD alternative*
Yes No  *Deflation of Y* Using either X or Y scores X scores Only  *Rotations
(W* matrix)* Both X and Y X only  *Raises exceptions for improper sizing of
input matrices* Yes No  *Provide Statistics (SPE, Hotelling's T)* No
Yes  *Provide
Statistical limits* No Yes  *Provide Variable importance to Projection (VIP)
* No Yes

In terms of performance, they should be same since the equations (for the
parts that are found in both) are identical and both use numpy arrays to do
calculations.  I think the best course of action would be to use Duchesnay's
code for the base, then add the missing parts from my code (statistics,
statistical limits, VIP) to help users in their analysis.  I will also get
some examples together that uses duchesnay's code with some of the
visualization functions to help users get a better understanding of PLS.

Also, I was thinking about incorporating some inline weave for the NIPALS
inner loop to improve the performance.  I haven't used it before, so I'll
have figure out the inner workings of weave first.

Please let me know what you think.

Thanks,
Aman
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scipy-dev/attachments/20110304/7f730688/attachment.html>