linear_MSD: Maximum Scatter Difference

In Rdimtools: Dimension Reduction and Estimation Methods

Maximum Scatter Difference

Description

Maximum Scatter Difference (MSD) is a supervised linear dimension reduction method. The basic idea of MSD is to use additive cost function rather than multiplicative trace ratio criterion that was adopted by LDA. Due to such formulation, it can neglect sample-sample-size problem from rank-deficiency of between-class variance matrix.

Usage

do.msd(
  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 variance.

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.

Author(s)

Kisung You

References

\insertRef

song_face_2007Rdimtools

Examples

## 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.msd(X, label, C=0.01)
out2 = do.msd(X, label, C=1)
out3 = do.msd(X, label, C=100)

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

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.