Rdimtools (version 0.3.2)

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 {
## generate data of 3 types with clear difference
dt1  = aux.gensamples(n=33)-50
dt2  = aux.gensamples(n=33)
dt3  = aux.gensamples(n=33)+50

## merge the data and create a label correspondingly
X      = rbind(dt1,dt2,dt3)
label  = c(rep(1,33), rep(2,33), rep(3,33))

## 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
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="AMMC::lambda=0.1")
plot(out2$Y[,1], out2$Y[,2], main="AMMC::lambda=1")
plot(out3$Y[,1], out3$Y[,2], main="AMMC::lambda=10")
# }
# NOT RUN {
# }