Rdimtools (version 1.0.0)

do.ammc: Adaptive Maximum Margin Criterion

Description

Adaptive Maximum Margin Criterion (AMMC) is a supervised linear dimension reduction method. The method uses different weights to characterize the different contributions of the training samples embedded in MMC framework. With the choice of a=0, b=0, and lambda=1, it is identical to standard MMC method.

Usage

do.ammc(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  a = 1,
  b = 1,
  lambda = 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.

a

tuning parameter for between-class weight in \([0,\infty)\).

b

tuning parameter for within-class weight in \([0,\infty)\).

lambda

balance parameter for between-class and within-class scatter matrices 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.

projection

a \((p\times ndim)\) whose columns are basis for projection.

References

lu_adaptive_2011Rdimtools

See Also

do.mmc

Examples

# NOT RUN {
## load iris data
data(iris)
X     = as.matrix(iris[,1:4])
label = as.factor(iris$Species)

## try different lambda values
out1 = do.ammc(X, label, lambda=0.1)
out2 = do.ammc(X, label, lambda=1)
out3 = do.ammc(X, label, lambda=10)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="AMMC::lambda=0.1", pch=19, cex=0.5, col=label)
plot(out2$Y, main="AMMC::lambda=1",   pch=19, cex=0.5, col=label)
plot(out3$Y, main="AMMC::lambda=10",  pch=19, cex=0.5, col=label)
par(opar)

# }