DA (version 1.2.0)

LFDA: Local Fisher Discriminant Analysis (LFDA)

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

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

%% ...

Details

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

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

# NOT RUN {
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)
# }