Kernel PCA
An S4 Class implementing Kernel PCA
Kernel PCA is a nonlinear extension of PCA using kernel methods.
funA function that does the embedding and returns a dimRedResult object.
stdparsThe 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 @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.
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
"rbfdot"
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.
Wraps around 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: AutoEncoder-class,
DRR-class,
DiffusionMaps-class,
DrL-class, FastICA-class,
FruchtermanReingold-class,
HLLE-class, Isomap-class,
KamadaKawai-class, LLE-class,
MDS-class, NNMF-class,
PCA-class, PCA_L1-class,
UMAP-class,
dimRedMethod-class,
dimRedMethodList, nMDS-class,
tSNE-class
## Not run:
dat <- loadDataSet("3D S Curve")
## use the S4 class directly:
kpca <- kPCA()
emb <- kpca@fun(dat, kpca@stdpars)
## simpler, use embed():
emb2 <- embed(dat, "kPCA")
plot(emb, type = "2vars")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.