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

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

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

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

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

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 maili