klfda: Kernel Local Fisher Discriminant Analysis for Supervised...

In lfda: Local Fisher Discriminant Analysis

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

k	n x n kernel matrix. Result of the kmatrixGauss function. n is the number of samples
y	n dimensional vector of class labels
r	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:

T	d x r transformation matrix (Z = t(T) * X)
Z	r x n matrix of dimensionality reduced samples

Author(s)

Yuan Tang

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

See Also

See lfda for the linear version.

Examples

k <- kmatrixGauss(iris[, -5])
y <- iris[, 5]
r <- 3
klfda(k, y, r, metric = "plain")

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