Prediction for Local Fisher Discriminant Analysis (LFDA)

Prediction function for Local Fisher Discriminant Analysis (LFDA)

predict.LFDA(object, newdata, prior, dimen, ...)

Arguments

object	The LFDA object
newdata	The newdata you want to predict
prior	The prior of the new data
dimen	The predicted dimen based on training model

Details

Value

class

The class labels from liner classifier

posterior

The posterior possibility from linear classifier

bayes_jud_pred

The predicted results using Mabayes classifier

Nbayes_assig_pred

The predicted results using Naive Bayes 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.

Author

qinxinghu@gmail.com

Note

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,newdata=iris[1:10,1:4],prior=NULL)