Rdimtools (version 0.3.2)

do.mmc: 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, preprocess = c("center", "scale", "cscale",
  "decorrelate", "whiten"))

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.

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

li_efficient_2006Rdimtools

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.mmc(X, label, ndim=2)
out2 = do.mmc(X, label, ndim=2, preprocess="decorrelate")

## visualize
par(mfrow=c(1,2))
plot(out1$Y[,1], out1$Y[,2], main="centering")
plot(out2$Y[,1], out2$Y[,2], main="decorrelating")

# }