Hi Betrand Thanks for the reply.
Well, what i have is n correlation matrices of the brain (n is the number of participants in the study). The simplest kernel computes the dot product between the n matrices. The kernel is further optimized using the NIPALS algorithm (as in Rosipal, Trejo 2002) The output y is multivariate with values indicating test scores from ADOS evaluations. On Fri, Mar 9, 2018 at 5:55 AM, bthirion <bertrand.thir...@inria.fr> wrote: > No this does not exist. It may be a good addition to the library, but > could you elaborate a bit on the use-case ? > > A workaround to this could be to provide PLS Regression a feature > representation that implictily embodies the kernel similarity. Accoding to > the chosen kernel, this can be easy or not. > Best, > > Bertrand Thirion > > On 08/03/2018 08:28, SJ JV wrote: > > I have to provide a list of customized kernels to the PLSRegression api. > Similar to the custom kernel support for SVM, is there support for > providing kernels to PLSRegression ? Can you make this available, if not ? > > Thanks > SV > > -- > U > > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- U
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