R/linear_DAGDNE.R
linear_DAGDNE.Rd
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.
an \((n\times p)\) matrix or data frame whose rows are observations.
a length-\(n\) vector of data class labels.
an integer-valued target dimension.
the number of neighboring points for k-nn graph construction.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a \((p\times ndim)\) whose columns are basis for projection.
Ding C, Zhang L (2015). “Double Adjacency Graphs-Based Discriminant Neighborhood Embedding.” Pattern Recognition, 48(5), 1734--1742.
## 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)