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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