Rdimtools (version 1.0.6)

do.lscore: Laplacian Score

Description

Laplacian Score (LSCORE) is an unsupervised linear feature extraction method. For each feature/variable, it computes Laplacian score based on an observation that data from the same class are often close to each other. Its power of locality preserving property is used, and the algorithm selects variables with smallest scores.

Usage

do.lscore(
  X,
  ndim = 2,
  type = c("proportion", 0.1),
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"),
  t = 10
)

Arguments

X

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

type

a vector of neighborhood graph construction. Following types are supported; c("knn",k), c("enn",radius), and c("proportion",ratio). Default is c("proportion",0.1), connecting about 1/10 of nearest data points among all data points. See also aux.graphnbd for more details.

preprocess

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

t

bandwidth parameter for heat kernel in \((0,\infty)\).

Value

a named list containing

Y

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

lscore

a length-\(p\) vector of laplacian scores. Indices with smallest values are selected.

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

he_laplacian_2005Rdimtools

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])

## try different kernel bandwidth
out1 = do.lscore(iris.dat, t=0.1)
out2 = do.lscore(iris.dat, t=1)
out3 = do.lscore(iris.dat, t=10)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=iris.lab, main="bandwidth=0.1")
plot(out2$Y, pch=19, col=iris.lab, main="bandwidth=1")
plot(out3$Y, pch=19, col=iris.lab, main="bandwidth=10")
par(opar)
# }
# NOT RUN {
# }