R help - Apr 2012 - kernlab kpca code

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) ?

(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?
)







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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