do.dagdne: Double-Adjacency Graphs-based Discriminant Neighborhood Embedding

Description

Doublue Adjacency Graphs-based Discriminant Neighborhood Embedding (DAG-DNE) is a variant of DNE. As its name suggests, it introduces two adjacency graphs for homogeneous and heterogeneous samples accordaing to their labels.

Usage

do.dagdne(
  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 \((n\times ndim)\) 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

ding_double_2015Rdimtools

Examples

Run this code

# NOT RUN {
## load iris data
data(iris)
X     = as.matrix(iris[,1:4])
label = as.factor(iris$Species)

## try different numbers for neighborhood size
out1 = do.dagdne(X, label, numk=5)
out2 = do.dagdne(X, label, numk=10)
out3 = do.dagdne(X, label, numk=25)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="nbd size=5", col=label, pch=19, cex=0.5)
plot(out2$Y, main="nbd size=10",col=label, pch=19, cex=0.5)
plot(out3$Y, main="nbd size=25",col=label, pch=19, cex=0.5)
par(opar)

# }