[R] kernlab kpca code

[Jessica Streicher]

Thanks a lot, totally forgot cran there.

Hm.. so they're multiplying some specifically computed Kernelmatrix with the pcv's.. interesting.. too tired to check the math there, guess i'll just accept its possible and go to sleep.

Am 26.04.2012 um 18:10 schrieb Steve Lianoglou:

> Hi Jessica,
> 
> On Thu, Apr 26, 2012 at 11:59 AM, Jessica Streicher
> <j.streicher at micromata.de> wrote:
>> Hi!
>> 
>> how do i get to the source code of kpca or even better predict.kpca(which it tells me doesn't exist but should) ?
> 
> Probably you have to do kernlab:::predict.kpca from your R workspace,
> but why not just download the source package and have at it?
> 
> http://cran.r-project.org/src/contrib/kernlab_0.9-14.tar.gz
> 
> HTH,
> -steve
> 
>> 
>> (And if anyone has too much time:
>> Now if i got that right, the @pcv attribute consists of the principal components, and for kpca, these are defined as projections of some random point x, which was transformed into the other feature space -> f(x), projected onto the actual PC (eigenvector of Covariance). This can be computed as the sum of the (eigenvectors of the Kernel matrix * the kernel function(sample_i,x))
>> 
>> Now assume i have some new points and want to project them, how can i do that with only having @pcv?
>> Wouldn't i rather need the eigenvectors of K?
>> )
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>>        [[alternative HTML version deleted]]
>> 
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> 
> 
> 
> -- 
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact