do.lscore  R Documentation 
Laplacian Score \insertCitehe_laplacian_2005Rdimtools 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.
do.lscore(X, ndim = 2, ...)
X 
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. 
ndim 
an integervalued target dimension (default: 2). 
... 
extra parameters including

a named Rdimtools
S3 object containing
an (n\times ndim) matrix whose rows are embedded observations.
a lengthp vector of laplacian scores. Indices with smallest values are selected.
a lengthndim vector of indices with highest scores.
a (p\times ndim) whose columns are basis for projection.
a list containing information for outofsample prediction.
name of the algorithm.
Kisung You
## 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)
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