nonlinear_KMMC: Kernel Maximum Margin Criterion

In Rdimtools: Dimension Reduction and Estimation Methods

Kernel Maximum Margin Criterion

Description

Kernel Maximum Margin Criterion (KMMC) is a nonlinear variant of MMC method using kernel trick. For computational simplicity, only the gaussian kernel is used with bandwidth parameter t.

Usage

do.kmmc(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "decorrelate", "whiten"),
  t = 1
)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
label	a length-n vector of data class labels.
ndim	an integer-valued target dimension.
preprocess	an additional option for preprocessing the data. Default is "center". See also aux.preprocess for more details.
t	bandwidth parameter for heat kernel in (0,∞).

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.

Author(s)

Kisung You

References

\insertRef

li_efficient_2006Rdimtools

See Also

do.mmc

Examples

## load iris data
data(iris)
set.seed(100)
subid = sample(1:150,100)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## perform MVP with different preprocessings
out1 = do.kmmc(X, label, t=0.1)
out2 = do.kmmc(X, label, t=1.0)
out3 = do.kmmc(X, label, t=10.0)

## visualize
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="bandwidth=0.1")
plot(out2$Y, pch=19, col=label, main="bandwidth=1")
plot(out3$Y, pch=19, col=label, main="bandwidth=10.0")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.