Predict function of Linear Fisher discriminant analysis of kernel principal components (DAKPC)

Predict function of Linear Fisher discriminant analysis of kernel principal components (DAKPC)

predict.LDAKPC(object = obj, prior, testData = data)

Arguments

object	The trained LDAKPC object
prior	The prior weight of the predicted data
testData	The data you want to test/predict

Details

Value

posterior

The posterior probabilities of the predicted data.

x

The discriminant function of the predicted data.

predicted_LDs

The predicted scores of discriminant function, is always the same with x if there is no transformation.

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References

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.

Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. (1999, August). Fisher discriminant analysis with kernels. In Neural networks for signal processing IX: Proceedings of the 1999 IEEE signal processing society workshop (cat. no. 98th8468) (pp. 41-48). Ieee.

Author

qinxinghu@gmail.com

Examples

data(iris)
train=LDAKPC(iris[,1:4],y=iris[,5],n.pc=3,kernel.name = "rbfdot")
pred=predict.LDAKPC(train,testData = iris[1:10,1:4])