Rdimtools (version 0.3.2)

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 {
## generate data of 3 types with clear difference
dt1  = aux.gensamples(n=33)-100
dt2  = aux.gensamples(n=33)
dt3  = aux.gensamples(n=33)+100

## merge the data and create a label correspondingly
X      = rbind(dt1,dt2,dt3)
label  = c(rep(1,33), rep(2,33), rep(3,33))

## compare Fisher score with LDA
out1 = do.lda(X, label)
out2 = do.fscore(X, label)

## visualize
par(mfrow=c(1,2))
plot(out1$Y[,1], out1$Y[,2], main="LDA")
plot(out2$Y[,1], out2$Y[,2], main="Fisher Score")
# }
# NOT RUN {
# }