[scikit-learn] Generalized Discriminant Analysis with Kernel

[Raga Markely]

Thank you very much for your info on Nystroem kernel approximator. I
appreciate it!

Best,
Raga

On Tue, Jan 10, 2017 at 7:47 AM, <scikit-learn-request at python.org> wrote:

> Send scikit-learn mailing list submissions to
>         scikit-learn at python.org
>
> To subscribe or unsubscribe via the World Wide Web, visit
>         https://mail.python.org/mailman/listinfo/scikit-learn
> or, via email, send a message with subject or body 'help' to
>         scikit-learn-request at python.org
>
> You can reach the person managing the list at
>         scikit-learn-owner at python.org
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of scikit-learn digest..."
>
> Date: Tue, 10 Jan 2017 11:58:59 +0300
> From: avn at mccme.ru
> To: Scikit-learn user and developer mailing list
>         <scikit-learn at python.org>
> Subject: Re: [scikit-learn] Generalized Discriminant Analysis with
>         Kernel
> Message-ID: <c2c15b0829e5facab0821dc078d90db1 at mccme.ru>
> Content-Type: text/plain; charset=UTF-8; format=flowed
>
> Hi Raga,
>
> You may try approximating your kernel using Nystroem kernel approximator
> (kernel_approximation.Nystroem) and then apply LDA to the transformed
> feature vectors. If you choose dimensionality of the target space
> (n_components) large enough (depending on your kernel and data),
> Nystroem approximator should provide sufficiently good kernel
> approximation for such combination to approximate GDA.
>
> Raga Markely ????? 2017-01-09 19:29:
> > Hello,
> >
> > I wonder if scikit-learn has implementation for generalized
> > discriminant analysis using kernel approach?
> > http://www.kernel-machines.org/papers/upload_21840_GDA.pdf
> >
> > I did some search, but couldn't find.
> >
> > Thank you,
> > Raga
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org
> > https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20170110/75d13f15/attachment.html>