[scikit-learn] Custom kernel for PLS Regression (using NIPALS algorithm)

[SJ JV]

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.thirion at 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
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-- 
U
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