Semi-Supervised Locally Discriminant Projection (SSLDP) is a semi-supervised
extension of LDP. It utilizes unlabeled data to overcome the small-sample-size problem
under the situation where labeled data have the small number. Using two information,
it both constructs the within- and between-class weight matrices incorporating the
neighborhood information of the data set.

```
do.ssldp(
X,
label,
ndim = 2,
type = c("proportion", 0.1),
preprocess = c("center", "scale", "cscale", "whiten", "decorrelate"),
beta = 0.5
)
```

## 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.

- 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 "center". See also `aux.preprocess`

for more details.

- beta
balancing parameter for intra- and inter-class information in \([0,1]\).

## 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 S, Lei Y, Wu Y (2011).
“Semi-Supervised Locally Discriminant Projection for Classification and Recognition.”
*Knowledge-Based Systems*, **24**(2), 341--346.

## Examples

```
## use iris data
data(iris)
X = as.matrix(iris[,1:4])
label = as.integer(iris$Species)
## copy a label and let 10% of elements be missing
nlabel = length(label)
nmissing = round(nlabel*0.10)
label_missing = label
label_missing[sample(1:nlabel, nmissing)]=NA
## compute with 3 different levels of 'beta' values
out1 = do.ssldp(X, label_missing, beta=0.1)
out2 = do.ssldp(X, label_missing, beta=0.5)
out3 = do.ssldp(X, label_missing, beta=0.9)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=label, main="SSLDP::beta=0.1")
plot(out2$Y, col=label, main="SSLDP::beta=0.5")
plot(out3$Y, col=label, main="SSLDP::beta=0.9")
par(opar)
```