R: Gaussian Kernel Computation (Particularly used in Kernel...
kmatrixGauss
R Documentation
Gaussian Kernel Computation
(Particularly used in Kernel Local Fisher Discriminant Analysis)
Description
Gaussian kernel computation for klfda, which maps the original
data space to non-linear and higher dimensions.
Usage
kmatrixGauss(x, sigma = 1)
Arguments
x
n x d matrix of original samples.
n is the number of samples.
sigma
dimensionality of reduced space. (default: 1)
Value
K n x n kernel matrix.
n is the number of samples.
Author(s)
Yuan Tang
References
Sugiyama, M (2007).
Dimensionality reduction of multimodal labeled data by
local Fisher discriminant analysis.
Journal of Machine Learning Research, vol.8, 1027–1061.
Sugiyama, M (2006).
Local Fisher discriminant analysis for supervised dimensionality reduction.
In W. W. Cohen and A. Moore (Eds.), Proceedings of 23rd International
Conference on Machine Learning (ICML2006), 905–912.