do.lscore {Rdimtools} | R Documentation |
Laplacian Score
Description
Laplacian Score (He et al. 2005) 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, ...)
Arguments
X |
an |
ndim |
an integer-valued target dimension (default: 2). |
... |
extra parameters including
|
Value
a named Rdimtools
S3 object 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.- 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.
Author(s)
Kisung You
References
He X, Cai D, Niyogi P (2005). “Laplacian Score for Feature Selection.” In Proceedings of the 18th International Conference on Neural Information Processing Systems, NIPS'05, 507–514.
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])
## 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)