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.

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

## Arguments

X

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

Zhang W, Xue X, Lu H, Guo Y (2006). “Discriminant Neighborhood Embedding for Classification.” Pattern Recognition, 39(11), 2240--2243.

Kisung You

## Examples

## 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
plot(out1$Y, main="DNE::nbd size=5", col=label, pch=19) plot(out2$Y, main="DNE::nbd size=10", col=label, pch=19)