feature_FSCORE: Fisher Score

In Rdimtools: Dimension Reduction and Estimation Methods

Fisher Score

Description

Fisher Score \insertCitefisher_use_1936Rdimtools 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, ...)

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.
...	extra parameters including preprocess an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

Value

a named Rdimtools S3 object containing

Y

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

featidx

a length-ndim vector of indices with highest scores.

projection

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

trfinfo

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

algorithm

name of the algorithm.

References

\insertAllCited

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 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$Y, pch=19, col=iris.lab, main="LDA")
plot(out2$Y, pch=19, col=iris.lab, main="Fisher Score")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.