| kPCA-class {dimRed} | R Documentation |
Kernel PCA
Description
An S4 Class implementing Kernel PCA
Details
Kernel PCA is a nonlinear extension of PCA using kernel methods.
Slots
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
General usage
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 @fun()
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
@stdpars.
Parameters
Kernel PCA can take the following parameters:
- ndim
the number of output dimensions, defaults to 2
- kernel
The kernel function, either as a function or a character vector with the name of the kernel. Defaults to
"rbfdot"- kpar
A list with the parameters for the kernel function, defaults to
list(sigma = 0.1)
The most comprehensive collection of kernel functions can be found in
kpca. In case the function does not take any
parameters kpar has to be an empty list.
Implementation
Wraps around kpca, but provides additionally
forward and backward projections.
References
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
See Also
Other dimensionality reduction methods:
AutoEncoder-class,
DRR-class,
DiffusionMaps-class,
DrL-class,
FastICA-class,
FruchtermanReingold-class,
HLLE-class,
Isomap-class,
KamadaKawai-class,
MDS-class,
NNMF-class,
PCA-class,
PCA_L1-class,
UMAP-class,
dimRedMethod-class,
dimRedMethodList(),
nMDS-class,
tSNE-class
Examples
## Not run:
if(requireNamespace("kernlab", quietly = TRUE)) {
dat <- loadDataSet("3D S Curve")
emb <- embed(dat, "kPCA")
plot(emb, type = "2vars")
}
## End(Not run)