do.wdfs {Rdimtools}

Worst-Case Discriminative Feature Selection

Description

As a supervised feature selection method, WDFS searches over all pairs of between-class and within-class scatters and chooses the highest-scoring features.

Usage

do.wdfs(
  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.

Author(s)

Kisung You

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

## 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.fscore(iris.dat, iris.lab)
out3 = do.wdfs(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="FSCORE")
plot(out3$Y, pch=19, col=iris.lab, main="WDFS")
par(opar)