Built upon `do.wdfs`

, this method selects features step-by-step to opt out the redundant sets
by iteratively update feature scores via scaling by the correlation between target and previously chosen variables.

```
do.uwdfs(
X,
label,
ndim = 2,
preprocess = c("null", "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 "null". See also `aux.preprocess`

for more details.

## Value

a named list containing

- Y
an \((n\times ndim)\) matrix whose rows are embedded observations.

- featidx
a length-\(ndim\) vector of indices with highest scores.

- trfinfo
a list containing information for out-of-sample prediction.

- projection
a \((p\times ndim)\) whose columns are basis for projection.

## References

Liao S, Gao Q, Nie F, Liu Y, Zhang X (2019).
“Worst-Case Discriminative Feature Selection.”
In *Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19*, 2973--2979.

## Examples

```
# \donttest{
## use iris data
## it is known that feature 3 and 4 are more important.
data(iris)
set.seed(100)
subid = sample(1:150,50)
iris.dat = as.matrix(iris[subid,1:4])
iris.lab = as.factor(iris[subid,5])
## compare with other algorithms
out1 = do.lda(iris.dat, iris.lab)
out2 = do.wdfs(iris.dat, iris.lab)
out3 = do.uwdfs(iris.dat, iris.lab)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=iris.lab, main="LDA")
plot(out2$Y, pch=19, col=iris.lab, main="WDFS")
plot(out3$Y, pch=19, col=iris.lab, main="UWDFS")
par(opar)
# }
```