Rdimtools (version 0.3.2)

do.kmmc: 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 \((0,\infty)\).

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.

References

li_efficient_2006Rdimtools

See Also

do.mmc

Examples

# NOT RUN {
## generate 3 different groups of data X and label vector
x1 = matrix(rnorm(4*10), nrow=10)-20
x2 = matrix(rnorm(4*10), nrow=10)
x3 = matrix(rnorm(4*10), nrow=10)+20
X  = rbind(x1, x2, x3)
label = c(rep(1,10), rep(2,10), rep(3,10))

## 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
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="bandwidth=0.1")
plot(out2$Y[,1], out2$Y[,2], main="bandwidth=1")
plot(out3$Y[,1], out3$Y[,2], main="bandwidth=10.0")

# }