klfda {lfda} | R Documentation |
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
k |
n x n kernel matrix. Result of the |
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")