Well, Kernel partial least squares can be used for regression as well as
for classification. It has been shown to work as good as Gaussian processes
for regression on head pose estimation applications. See for example this
CVPR paper
http://iselab.cvc.uab.es/files/Publications/2012/PDF/AGD2012.pdf
It can be used as a classification machine which I frequently use. See this
paper:
http://enpub.fulton.asu.edu/cseml/06summer/pls_dis.pdf
if you would like a brief report on its performance, I can run experiments
on the datasets you like and provide the results on its performance as a
classification machine, for both PLS and KPLS.
For regression, I cant think right now of a benchmark for that, however, I
would be happy if you suggest any.
Regards,
Eweiwi
On Fri, Nov 29, 2013 at 6:54 PM, Olivier Grisel <olivier.gri...@ensta.org>wrote:
> 2013/11/29 abdalrahman eweiwi <abdalrahman.ewe...@gmail.com>:
> > Hi,
> >
> > First I would like to thank you all for this great and handy machine
> > learning toolkit. Second, would it be of an interest to add an
> > implementation for Kernel Partial Least Squares for the library?
>
> Hi Eweiwi, I am not familiar with Kernel PLS myself. What kind of
> tasks it it known to work well for?
>
> Do you think one of the existing example could demonstrate its
> capability? If not is there a small datatset that can be used to
> demonstrate interesting features of this algorithm?
>
> --
> Olivier
>
>
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