kmatrixGauss: Gaussian Kernel Computation (Particularly used in Kernel...

In lfda: 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.

https://shapeofdata.wordpress.com/2013/07/23/gaussian-kernels/

See Also

See klfda for the computation of kernel local fisher discriminant analysis

Examples

kmatrixGauss(iris[, -5])

lfda documentation built on Aug. 1, 2019, 1:04 a.m.