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-requ...@python.org> wrote: > Send scikit-learn mailing list submissions to > scikit-learn@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-requ...@python.org > > You can reach the person managing the list at > scikit-learn-ow...@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: a...@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: <c2c15b0829e5facab0821dc078d90...@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 >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
