do.dne: Discriminant Neighborhood Embedding

Description

Discriminant Neighborhood Embedding (DNE) is a supervised subspace learning method. DNE tries to move multi-class data points in high-dimensional space in accordance with local intra-class attraction and inter-class repulsion.

Usage

do.dne(
  X,
  label,
  ndim = 2,
  numk = max(ceiling(nrow(X)/10), 2),
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

an $(n \times p)$ matrix or data frame whose rows are observations.

label

a length- $n$ vector of data class labels.

ndim

an integer-valued target dimension.

numk

the number of neighboring points for k-nn graph construction.

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 $(n \times n d i m)$ matrix whose rows are embedded observations.
trfinfo: a list containing information for out-of-sample prediction.
projection: a $(p \times n d i m)$ whose columns are basis for projection.

References

zhang_discriminant_2006Rdimtools

Examples

Run this code

# NOT RUN {
## load iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## try different numbers for neighborhood size
out1 = do.dne(X, label, numk=5)
out2 = do.dne(X, label, numk=10)
out3 = do.dne(X, label, numk=20)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="DNE::nbd size=5",  col=label, pch=19)
plot(out2$Y, main="DNE::nbd size=10", col=label, pch=19)
plot(out3$Y, main="DNE::nbd size=20", col=label, pch=19)
par(opar)

# }