do.lsls: Locality Sensitive Laplacian Score

Description

Locality Sensitive Laplacian Score (LSLS) is a supervised linear feature extraction method that combines a feature selection framework of laplacian score where the graph laplacian is adjusted as in the scheme of LSDA. The adjustment is taken via decomposed affinity matrices which are separately constructed using the provided class label information.

Usage

do.lsls(
  X,
  label,
  ndim = 2,
  alpha = 0.5,
  k = 5,
  preprocess = c("null", "center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

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.

alpha

a weight factor; should be a real number in \([0,1]\).

an integer; the size of a neighborhood.

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

liao_gene_2014aRdimtools

Examples

Run this code

# 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 different neighborhood sizes
out1 = do.lsls(iris.dat, iris.lab, k=3)
out2 = do.lsls(iris.dat, iris.lab, k=6)
out3 = do.lsls(iris.dat, iris.lab, k=9)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=iris.lab, pch=19, main="LSLS::k=3")
plot(out2$Y, col=iris.lab, pch=19, main="LSLS::k=6")
plot(out3$Y, col=iris.lab, pch=19, main="LSLS::k=9")
par(opar)
# }
# NOT RUN {
# }