feature_UWDFS: Uncorrelated Worst-Case Discriminative Feature Selection

In kisungyou/Rdimtools: Dimension Reduction and Estimation Methods

Uncorrelated Worst-Case Discriminative Feature Selection

Description

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.

Usage

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.

Author(s)

Kisung You

References

\insertRef

liao_worstcase_2019Rdimtools

See Also

do.wdfs

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.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)

kisungyou/Rdimtools documentation built on Jan. 2, 2023, 9:55 a.m.