do.mmc {Rdimtools}

Maximum Margin Criterion

Description

Maximum Margin Criterion (MMC) is a linear supervised dimension reduction method that maximizes average margin between classes. The cost function is defined as

trace(S_b - S_w)

where S_b is an overall variance of class mean vectors, and S_w refers to spread of every class. Note that Principal Component Analysis (PCA) maximizes total scatter, S_t = S_b + S_w.

Usage

do.mmc(X, label, ndim = 2)

Arguments

X

an (n\times p) matrix whose rows are observations and columns represent independent variables.

label

a length-n vector of data class labels.

ndim

an integer-valued target dimension.

Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

projection

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

algorithm

name of the algorithm.

Author(s)

Kisung You

References

Li H, Jiang T, Zhang K (2006). “Efficient and Robust Feature Extraction by Maximum Margin Criterion.” IEEE Transactions on Neural Networks, 17(1), 157–165.

Examples

## use iris data
data(iris, package="Rdimtools")
subid = sample(1:150, 50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## compare MMC with other methods
outMMC = do.mmc(X, label)
outMVP = do.mvp(X, label)
outPCA = do.pca(X)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(outMMC$Y, pch=19, col=label, main="MMC")
plot(outMVP$Y, pch=19, col=label, main="MVP")
plot(outPCA$Y, pch=19, col=label, main="PCA")
par(opar)