Supervised Locality Pursuit Embedding (SLPE) is a supervised extension of LPE
that uses class labels of data points in order to enhance discriminating power in
its mapping into a low dimensional space.

```
do.slpe(
X,
label,
ndim = 2,
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)
```

## Arguments

- X
an \((n\times p)\) matrix or data frame whose rows are observations
and columns represent independent variables.

- label
a length-\(n\) vector of data class labels.

- ndim
an integer-valued target dimension.

- 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

Zheng Z, Yang J (2006).
“Supervised Locality Pursuit Embedding for Pattern Classification.”
*Image and Vision Computing*, **24**(8), 819--826.

## Examples

```
## use 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])
## compare SLPE with SLPP
out1 <- do.slpp(X, label)
out2 <- do.slpe(X, label)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, pch=19, col=label, main="SLPP")
plot(out2$Y, pch=19, col=label, main="SLPE")
par(opar)
```