gkm: Kernel Matrix Using Guasian Kernel

In RKUM: Robust Kernel Unsupervised Methods

Kernel Matrix Using Guasian Kernel

Description

Many radial basis function kernels, such as the Gaussian kernel, map X into a infinte dimensional space. While the Gaussian kernel has a free parameter (bandwidth), it still follows a number of theoretical properties such as boundedness, consistence, universality, robustness etc. It is the most applicable kernel of the positive definite kernel based methods.

Usage

gkm(X)

Arguments

X	a data matrix.

Details

Many radial basis function kernels, such as the Gaussian kernel, map input sapce into a infinite dimensional space. The Gaussian kernel has a a number of theoretical properties such as boundedness, consistence, universality and robustness, etc.

Value

K	a Gram/ kernel matrix

Author(s)

Md Ashad Alam <[e-mail]>

References

Md. Ashad Alam, Hui-Yi Lin, HOng-Wen Deng, Vince Calhour Yu-Ping Wang (2018), A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia, Journal of Neuroscience Methods, Vol. 309, 161-174.

Md Ashad Alam, Kenji Fukumizu and Yu-Ping Wang (2018), Influence Function and Robust Variant of Kernel Canonical Correlation Analysis, Neurocomputing, Vol. 304 (2018) 12-29.

M. Romanazzi (1992), Influence in canonical correlation analysis, Psychometrika vol 57(2) (1992) 237-259.

Examples

##Dummy data:
X<-matrix(rnorm(1000),100)
gkm(X)

RKUM documentation built on June 22, 2022, 9:06 a.m.