| kmatrixGauss {lfda} | 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.
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])