Rdimtools (version 1.0.6)

do.mmsd: Multiple Maximum Scatter Difference

Description

Multiple Maximum Scatter Difference (MMSD) is a supervised linear dimension reduction method. It is a variant of MSD in that discriminant vectors are orthonormal. Similar to MSD, it also does not suffer from rank deficiency issue of scatter matrix.

Usage

do.mmsd(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "whiten", "decorrelate"),
  C = 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.

C

nonnegative balancing parameter for intra- and inter-class scatter.

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

fengxisong_multiple_2007Rdimtools

Examples

# NOT RUN {
## generate data of 3 types with clear difference
set.seed(100)
dt1  = aux.gensamples(n=20)-50
dt2  = aux.gensamples(n=20)
dt3  = aux.gensamples(n=20)+50

## merge the data and create a label correspondingly
X      = rbind(dt1,dt2,dt3)
label  = rep(1:3, each=20)

## try different balancing parameter
out1 = do.mmsd(X, label, C=0.01)
out2 = do.mmsd(X, label, C=1)
out3 = do.mmsd(X, label, C=100)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="MMSD::C=0.01")
plot(out2$Y, pch=19, col=label, main="MMSD::C=1")
plot(out3$Y, pch=19, col=label, main="MMSD::C=100")
par(opar)

# }