do.keca {Rdimtools} | R Documentation |
Kernel Entropy Component Analysis
Description
Kernel Entropy Component Analysis(KECA) is a kernel method of dimensionality reduction.
Unlike Kernel PCA(do.kpca
), it utilizes eigenbasis of kernel matrix K
in accordance with indices of largest Renyi quadratic entropy in which entropy for
j
-th eigenpair is defined to be \sqrt{\lambda_j}e_j^T 1_n
, where e_j
is
j
-th eigenvector of an uncentered kernel matrix K
.
Usage
do.keca(
X,
ndim = 2,
kernel = c("gaussian", 1),
preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate")
)
Arguments
X |
an |
ndim |
an integer-valued target dimension. |
kernel |
a vector containing name of a kernel and corresponding parameters. See also |
preprocess |
an additional option for preprocessing the data.
Default is "null". See also |
Value
a named list containing
- Y
an
(n\times ndim)
matrix whose rows are embedded observations.- trfinfo
a list containing information for out-of-sample prediction.
- entropy
a length-
ndim
vector of estimated entropy values.
Author(s)
Kisung You
References
Jenssen R (2010). “Kernel Entropy Component Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5), 847–860.
See Also
Examples
## load iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])
## 1. standard KECA with gaussian kernel
output1 <- do.keca(X,ndim=2)
## 2. gaussian kernel with large bandwidth
output2 <- do.keca(X,ndim=2,kernel=c("gaussian",5))
## 3. use laplacian kernel
output3 <- do.keca(X,ndim=2,kernel=c("laplacian",1))
## Visualize three different projections
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(output1$Y, pch=19, col=label, main="Gaussian kernel")
plot(output2$Y, pch=19, col=label, main="Gaussian, sigma=5")
plot(output3$Y, pch=19, col=label, main="Laplacian kernel")
par(opar)