rtemis (version 0.79)

d.KPCA: 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, scale = TRUE, center = FALSE, 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

String: Type of kernel to use. See kernlab::kpca

kpar

List of hyperparameters: See kernlab::kpca("kpar")

scale

Logical: If TRUE, scale input data before projecting

center

Logical: If TRUE, also center input data if scale is TRUE

verbose

Logical: If TRUE, print messages to screen. Default = TRUE

...

Additional parameters to be passed to fastKPCA::fastKPCA

Value

rtDecom object

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.

See Also

Other Decomposition: d.CUR, d.H2OAE, d.H2OGLRM, d.ICA, d.ISOMAP, d.LLE, d.MDS, d.NMF, d.PCA, d.SPCA, d.SVD, d.TSNE, d.UMAP