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.

```
do.dagdne(
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

Ding C, Zhang L (2015).
“Double Adjacency Graphs-Based Discriminant Neighborhood Embedding.”
*Pattern Recognition*, **48**(5), 1734--1742.

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