LFDA: Local Fisher Discriminant Analysis (LFDA)

In xinghuq/DA: Discriminant Analysis for Evolutionary Inference

Description

This function implements local Fisher discriminant analysis. It gives the discriminant function with the posterior possibility of each class.

Usage

LFDA(x, y, r, prior = proportions,
CV = FALSE, usekernel = TRUE, fL = 0, 
tol, kernel = "gaussian", 
metric = c("orthonormalized", "plain", "weighted"), 
knn = 5, ...)

Arguments

x	Input training data
y	Training labels
r	Number of reduced features that will be kept
prior	Prior possibility of each class
CV	Whether to do cross validation
usekernel	Whether to use the kernel discrimination in native bayes classifier
fL	Feed to native bayes classifier. Factor for Laplace correction, default factor is 0, i.e. no correction.
tol	The tolerance used in Mabayes discrimination, see Mabayes
kernel	If usekernel is TRUE, specifying the kernel names, see NaiveBaye.
metric	The type of metric in the embedding space (no default), e.g., 'weighted', weighted eigenvectors; 'orthonormalized' , orthonormalized; 'plain', raw eigenvectors.
knn	Number of nearest neighbors
...	additional arguments for the classifier

Details

The results give the classified classes and the posterior possibility of each class using different classifier.

Value

class	The class labels
posterior	The posterior possibility of each class
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.

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.

Examples

LFDAtest=LFDA(iris[,1:4],y=iris[,5],r=3, 
CV=FALSE,usekernel = TRUE, fL = 0,
kernel="gaussian",metric = "plain",knn = 6,tol = 1)
LFDApred=predict.LFDA(LFDAtest,iris[1:10,1:4],prior=NULL)

xinghuq/DA documentation built on July 11, 2021, 8:49 a.m.