one_kpca: One-sided kernel principal component analysis

In loadings: Loadings for Principal Component Analysis and Partial Least Squares

One-sided kernel principal component analysis

Description

This function performs one-sided kernel principal component analysis (one-sided KPCA). In this function, data matrix is automatically scaled to zero mean and unit variance (i.e. autoscaling) for each variables.

Usage

one_kpca(X,K)

Arguments

X	Data matrix that include variables in each columns.
K	Kernel matrix computed from the data matrix X.

Details

The kernel matrix K, which is the argument of the one_kpca function, must be centered.

Value

The return value is a list object that contains the following elements:

P : A matrix with one-sided KPCA loading in each column

T : A matrix with one-sided KPCA score for linear side in each column

U : A matrix with one-sied KPCA score for nonlinear side in each column

Author(s)

Hiroyuki Yamamoto

References

Yamamoto H. (2023) One-sided Kernel Principal Component Analysis, Jxiv, <doi:10.51094/jxiv.262>.

Examples

# COVID-19 metabolome data
data(covid19)

X <- covid19$X$metabolomics
K <- covid19$K$metabolomics

onekpca <- one_kpca(X,K)

loadings documentation built on May 29, 2024, 8:01 a.m.