kfda.predict {kfda}

Predict Method for Kernel Fisher Discriminant Analysis (KFDA) fit

Description

Test the testData using KFDA. This function is used after training phase is performed using the kfda function.

Usage

kfda.predict(object = obj, testData = data)

Arguments

object

An R object of class kfda.

testData

an optional data frame or matrix containing the variables in the model. In particular, the order of variables in the data frame must be the same as trainData, and the target value must be removed in advance.

Details

Since this function inherits KPCA and LDA, various learning can be possible by adjusting the hyper-parameters of each function.

Value

The result of performing testData on the KFDA model.

class

A class label of testData.

posterior

A posterior probabilities for the classes.

x

The scores of testData on up to kfda discriminant variables.

Author(s)

Donghwan Kim
[e-mail] [e-mail] [e-mail]

References

Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>. Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recognition, 37(10): 2097-2100.

See Also

kfda

Examples

# data input
data(iris)

# data separation
idx <- sample(1:dim(iris)[1], round(dim(iris)[1]*0.7))
trainData <- iris[idx, ]
testData <- iris[-(idx), -dim(iris)[2]]
testData.Label <- iris[-(idx), dim(iris)[2]]

# training KFDA model
kfda.model <- kfda(trainData = trainData, kernel.name = "rbfdot")

# testing new(test)data by KFDA model
pre <- kfda.predict(object = kfda.model, testData = testData)

# plotting
plot(kfda.model$LDs, col = kfda.model$label, pch = 19, main = "Plot for KFDA")
points(pre$x, col = pre$class, cex = 2)
legend("topleft", legend = c("trainData","testData"), pch = c(19,1))

# prediction result
table(pre$class, (testData.Label))