Rdimtools (version 0.3.2)

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

fengxi_song_multiple_2007Rdimtools

Examples

# NOT RUN {
## generate data of 3 types with clear difference
dt1  = aux.gensamples(n=33)-100
dt2  = aux.gensamples(n=33)
dt3  = aux.gensamples(n=33)+100

## 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 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
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="MMSD::C=0.01")
plot(out2$Y[,1], out2$Y[,2], main="MMSD::C=1")
plot(out3$Y[,1], out3$Y[,2], main="MMSD::C=100")
# }
# NOT RUN {
# }