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@python.org> wrote:

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Date: Tue, 10 Jan 2017 11:58:59 +0300 From: avn@mccme.ru To: Scikit-learn user and developer mailing list <scikit-learn@python.org> Subject: Re: [scikit-learn] Generalized Discriminant Analysis with Kernel Message-ID: <c2c15b0829e5facab0821dc078d90db1@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@python.org https://mail.python.org/mailman/listinfo/scikit-learn