Rdimtools (version 0.3.2)

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

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_discriminant_2006Rdimtools

Examples

# NOT RUN {
#' @examples
## 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
Y      = rbind(dt1,dt2,dt3)
label  = c(rep(1,33), rep(2,33), rep(3,33))

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

## visualize
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="nbd size=5")
plot(out2$Y[,1], out2$Y[,2], main="nbd size=10")
plot(out3$Y[,1], out3$Y[,2], main="nbd size=25")

# }