Rdimtools (version 1.0.6)

do.fscore: Fisher Score

Description

Fisher Score (FSCORE) is a supervised linear feature extraction method. For each feature/variable, it computes Fisher score, a ratio of between-class variance to within-class variance. The algorithm selects variables with largest Fisher scores and returns an indicator projection matrix.

Usage

do.fscore(
  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

fisher_use_1936Rdimtools

Examples

# NOT RUN {
## 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 Fisher score with LDA
out1 = do.lda(iris.dat, iris.lab)
out2 = do.fscore(iris.dat, iris.lab)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, col=iris.lab, main="LDA")
plot(out2$Y, col=iris.lab, main="Fisher Score")
par(opar)

# }