The predict method for KLFDA(klfda_1)

Prediction function for Kernel Local Fisher Discriminant Analysis KLFDA(klfda_1). The prediction uses three classifiers, as explained in the manual of klfda_1.

predict.klfda_1(object, newdata, dimen, ...)

Arguments

object	The object from Kernel Local Fisher Discriminant Analysis KLFDA(klfda_1).
newdata	The test data. The data you want to predict.
dimen	The data dimen, if NULL, the function will automatically learns from the data.

Details

Value

The values include the class and posterior possibility of each class predicted by three classifiers.

class

The class labels from liner 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

The reduced features

%% ...

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.

Author

qinxinghu@gmail.com

Examples