qtSNE-class: Class "qtSNE"

In qkerntool: Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools

Description

An S4 Class for qtSNE.

Details

The qtSNE is a method that uses Qkernel t-Distributed Stochastic Neighborhood Embedding between the distance matrices in high and low-dimensional space to embed the data. The method is very well suited to visualize complex structures in low dimensions.

Objects from the Class

Objects can be created by calls of the form new("qtSNE", ...). or by calling the function qtSNE.

Slots

dimRed

Matrix containing the new representations for the objects after qtSNE

cndkernf

The kernel function used

Method

dimRed

signature(object="qtSNE"): return a new representation matrix

cndkernf

signature(object="qtSNE"): return the kernel used

Author(s)

Yusen Zhang
[e-mail]

References

Maaten, L. van der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15, 3221-3245.

van der Maaten, L., Hinton, G., 2008. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579-2605.

See Also

qtSNE

Examples

## Not run: 
#use iris data set
data(iris)
testset <- sample(1:150,20)
train <- as.matrix(iris[,1:4])

colors = rainbow(length(unique(iris$Species)))
names(colors) = unique(iris$Species)
#for matrix
ecb = function(x,y){
  plot(x,t='n');
  text(x,labels=iris$Species, col=colors[iris$Species])
}
kpc2 <- qtSNE(train, kernel = "rbfbase", qpar = list(sigma=1,q=0.8),
              epoch_callback = ecb, perplexity=10, max_iter = 500)

#cndernf
cndkernf(kpc2)

#dimRed
plot(dimRed(kpc2),col=train)


## End(Not run)

qkerntool documentation built on May 2, 2019, 6:11 a.m.