d_KPCA: Kernel Principal Component Analysis

In egenn/rtemis: Machine Learning and Visualization

Kernel Principal Component Analysis

Description

Perform kernel PCA decomposition using kernlab::kpca

Usage

d_KPCA(
  x,
  x.test = NULL,
  k = 2,
  th = 1e-04,
  kernel = "rbfdot",
  kpar = NULL,
  center = TRUE,
  scale = TRUE,
  verbose = TRUE,
  ...
)

Arguments

x	Input data
x.test	Optional test set. Will be projected on to KPCA basis
k	Integer vector of length 1 or greater. N of components to return If set to 0, th determines eigenvalue below which PCs are ignored
th	Threshold for eigenvalue below which PCs are ignored if k is set to 0
kernel	Character: Type of kernel to use. See kernlab::kpca
kpar	List of hyperparameters: See kernlab::kpca("kpar")
center	Logical: If TRUE, center data prior to decomposition. Default = TRUE
scale	Logical: If TRUE, scale data prior to decomposition. Default = TRUE
verbose	Logical: If TRUE, print messages to screen. Default = TRUE
...	Additional parameters to be passed to fastKPCA::fastKPCA

Details

Project scaled variables to KPCA components. Input must be n by p, where n represents number of cases, and p represents number of features. KPCA will be applied to the transpose of the n x p matrix.

Value

rtDecom object

Author(s)

E.D. Gennatas

See Also

Other Decomposition: d_H2OAE(), d_H2OGLRM(), d_ICA(), d_Isomap(), d_LLE(), d_MDS(), d_NMF(), d_PCA(), d_SPCA(), d_SVD(), d_TSNE(), d_UMAP()

egenn/rtemis documentation built on Nov. 22, 2024, 4:12 a.m.