kernel.pca: Kernel Principal Components Analysis

In mixKernel: Omics Data Integration Using Kernel Methods

Kernel Principal Components Analysis

Description

Performs a kernel PCA.

Usage

kernel.pca(K, ncomp = nrow(K$kernel))

Arguments

K	a kernel object obtained using either compute.kernel or combine.kernels.
ncomp	integer. Indicates the number of components to return..

Value

kernel.pca returns an object of classes "kernel.pca" and "pca", which is a list containing the following entries:

• ncomp: the number of principal components;

• X: the input kernel matrix;

• kernel: the input kernel object provided by the user;

• sdev: the singular values (square root of the eigenvalues);

• rotation: the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors);

• loadings: same as 'rotation' to keep the mixOmics spirit;

• x: same as 'rotation' to keep the mixOmics spirit;

Author(s)

Jerome Mariette <[e-mail]> Nathalie Vialaneix <[e-mail]>

References

Scholkopf B., Smola A. and Muller K.R. (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319.

See Also

compute.kernel, combine.kernels

Examples

data(TARAoceans)
phychem.kernel <- compute.kernel(TARAoceans$phychem, kernel.func = "linear")
kernel.pca.result <- kernel.pca(phychem.kernel, ncomp = 3)

mixKernel documentation built on Sept. 18, 2023, 5:16 p.m.