lfda (version 1.1.1)

kmatrixGauss: 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.

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

## Not run: 
# k <- kmatrixGauss(x = train.data)
# ## End(Not run)