KLFDA_mk: Kernel Local Fisher Discriminant Analysis (KLFDA) with...

In DA: Discriminant Analysis for Evolutionary Inference

Description

Kernel Local Fisher Discriminant Analysis (KLFDA). This function implements the Kernel Local Fisher Discriminant Analysis with a Multinomial kernel.

Usage

KLFDA_mk(X, Y, r, order, regParam, 
usekernel = TRUE, fL = 0.5, 
priors, tol, reg, metric, 
plotFigures = FALSE, verbose, ...)

Arguments

X	The input training data
Y	The training labels
r	The number of reduced features
order	The order passing to Multinomial Kernel
regParam	The regularization parameter for kernel matrix
usekernel	Whether to used kernel classifier
fL	pass to kernel classifier if usekenel is TRUE
priors	The weight of each class
tol	The tolerance for rejecting uni-variance
reg	The regularization parameter
metric	Type of metric in the embedding space (default: 'weighted') 'weighted' - weighted eigenvectors 'orthonormalized' - orthonormalized 'plain' - raw eigenvectors
plotFigures	whether to plot the reduced features, 3D plot
verbose	silence the processing
...	additional arguments for the classifier

Details

This function uses Multinomial Kernel, users can replace the Multinomial Kernel based on your own purpose. The final discrimination employs three classifiers, the basic linear classifier, the Mabayes (Bayes rule and the Mahalanobis distance), and Niave Bayes classifier.

Value

class	The class labels from linear classifier
posterior	The posterior possibility of each class from linear classifier
bayes_judgement	Discrimintion results using the Mabayes classifier
bayes_assigment	Discrimintion results using the Naive bayes classifier
Z	The reduced features

Author(s)

[e-mail]

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.

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

Tang, Y., & Li, W. (2019). lfda: Local Fisher Discriminant Analysis inR. Journal of Open Source Software, 4(39), 1572.

Moore, A. W. (2004). Naive Bayes Classifiers. In School of Computer Science. Carnegie Mellon University.

Pierre Enel (2020). Kernel Fisher Discriminant Analysis (https://www.github.com/p-enel/MatlabKFDA), GitHub. Retrieved March 30, 2020.

Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2004). kernlab-an S4 package for kernel methods in R. Journal of statistical software, 11(9), 1-20.

See Also

predict.KLFDA_mk, klfda_1

Examples

btest=KLFDA_mk(X=as.matrix(iris[,1:4]),
Y=as.matrix(as.data.frame(iris[,5])),r=3,order=2,regParam=0.25, 
usekernel=TRUE,fL=0.5,
priors=NULL,tol=1e-90,reg=0.01,metric =  'plain',plotFigures=FALSE,
verbose=TRUE)
#pred=predict.KLFDA_mk(btest,as.matrix(iris[1:10,1:4]))

DA documentation built on July 12, 2021, 9:07 a.m.