Constrained Graph Embedding (CGE) is a semi-supervised embedding method that incorporates
partially available label information into the graph structure that find embeddings
consistent with the labels.

```
do.cge(
X,
label,
ndim = 2,
type = c("proportion", 0.1),
preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate")
)
```

## Arguments

- X
an \((n\times p)\) matrix or data frame whose rows are observations

- label
a length-\(n\) vector of data class labels. It should contain `NA`

elements for missing label.

- ndim
an integer-valued target dimension.

- type
a vector of neighborhood graph construction. Following types are supported;
`c("knn",k)`

, `c("enn",radius)`

, and `c("proportion",ratio)`

.
Default is `c("proportion",0.1)`

, connecting about 1/10 of nearest data points
among all data points. See also `aux.graphnbd`

for more details.

- preprocess
an additional option for preprocessing the data.
Default is `"null"`

. 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.

## References

He X, Ji M, Bao H (2009).
“Graph Embedding with Constraints.”
In *IJCAI*.

## Examples

```
## use iris data
data(iris)
X = as.matrix(iris[,2:4])
label = as.integer(iris[,5])
lcols = as.factor(label)
## copy a label and let 10% of elements be missing
nlabel = length(label)
nmissing = round(nlabel*0.10)
label_missing = label
label_missing[sample(1:nlabel, nmissing)]=NA
## try different neighborhood sizes
out1 = do.cge(X, label_missing, type=c("proportion",0.10))
out2 = do.cge(X, label_missing, type=c("proportion",0.25))
out3 = do.cge(X, label_missing, type=c("proportion",0.50))
## visualize
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="10% connected", pch=19, col=lcols)
plot(out2$Y, main="25% connected", pch=19, col=lcols)
plot(out3$Y, main="50% connected", pch=19, col=lcols)
par(opar)
```