Oops, I forgot to ask the question:
Does anyone know what could be the problem? Am I using the methods
incorrectly or is there a formulation difference? I know that sklearn's
PLSregression uses the NIPALS agorithm while Matlab uses SIMPLS. However, I
figured that a change in algorithm wouldn't affect the outputs by a
non-negligible scale
Thanks
- Fernando
On Mon, Jun 20, 2016 at 11:04 AM, Fernando Quivira <f.quiv...@gmail.com>
wrote:
> Hi
>
> I'm trying to replicate some dimension reduction results computed with
> Matlab's plsregress using sklearn's PLSRegression. However, I'm finding
> that the output of the transform method in sklearn's PLSRegression differs
> from Matlab results by a constant scale factor across each component
> (constant across features but different across components).
>
> I used some dummy data that I could load in Matlab to test this. I found
> that if I normalized (with zscores) the sklearn and Matlab's outputs, I got
> the same results (see attached figures). I have attached the code that can
> replicate this. The whole test can be run from testPLS.m (you need matlab
> 2014+).
>
> I'm using python3.5 64bit in Windows with the Anaconda environment and
> sklearn 0.17.1-np110py35_1
>
> Thanks
>
> - Fernando
>
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