klfda: Kernel Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction

Description

Performs kernel local fisher discriminant analysis on the given data, which is the non-linear version of LFDA (see details lfda).

Usage

klfda(k, y, r, metric = c("weighted", "orthonormalized", "plain"),
  knn = 6, reg = 0.001)

Arguments

n x n kernel matrix. Result of the kmatrixGauss function. n is the number of samples

n dimensional vector of class labels

dimensionality of reduced space (default: d)

metric

type of metric in the embedding space (default: 'weighted') 'weighted' --- weighted eigenvectors 'orthonormalized' --- orthonormalized 'plain' --- raw eigenvectors

knn

parameter used in local scaling method (default: 6)

reg

regularization parameter (default: 0.001)

Value

list of the LFDA results:

d x r transformation matrix (Z = t(T) * X)

r x n matrix of dimensionality reduced samples

References

Sugiyama, M (2007). - contain implementation 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.

Original Matlab Implementation: http://www.ms.k.u-tokyo.ac.jp/software.html#LFDA

Examples

Run this code

# NOT RUN {
k <- kmatrixGauss(iris[, -5])
y <- iris[, 5]
r <- 3
klfda(k, y, r, metric = "plain")
# }