An S4 Class implementing Kernel PCA
Kernel PCA is a nonlinear extension of PCA using kernel methods.
A function that does the embedding and returns a dimRedResult object.
The standard parameters for the function.
Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the
slot, or the method name be passed to the embed function and
parameters can be given to the
..., in which case
missing parameters will be replaced by the ones in the
Kernel PCA can take the following parameters:
the number of output dimensions, defaults to 2
The kernel function, either as a function or a
character vector with the name of the kernel. Defaults to
A list with the parameters for the kernel function,
list(sigma = 0.1)
The most comprehensive collection of kernel functions can be found in
kpca. In case the function does not take any
kpar has to be an empty list.
kpca, but provides additionally
forward and backward projections.
Sch\"olkopf, B., Smola, A., M\"uller, K.-R., 1998. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299-1319. https://doi.org/10.1162/089976698300017467
Other dimensionality reduction methods:
1 2 3 4 5 6 7 8 9 10 11 12 13
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