Local Linear Laplacian Eigenmaps is an unsupervised manifold learning method as an
extension of Local Linear Embedding (do.lle
). It is claimed to be
more robust to local structure and noises. It involves the concept of
artificial neighborhood in constructing the adjacency graph for reconstruction of
the approximated manifold.
an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.
an integer-valued target dimension.
an additional option for preprocessing the data.
Default is "null"
. See also aux.preprocess
for more details.
size of near neighborhood for each data point.
size of artifical neighborhood.
scale parameter for Gaussian kernel. It should be in \((0,1)\).
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
Liu F, Zhang W, Gu S (2016). “Local Linear Laplacian Eigenmaps: A Direct Extension of LLE.” Pattern Recognition Letters, 75, 30--35.
if (FALSE) {
## use iris data
data(iris)
X = as.matrix(iris[,1:4])
label = as.integer(iris$Species)
# see the effect bandwidth
out1 = do.llle(X, bandwidth=0.1, P=20)
out2 = do.llle(X, bandwidth=0.5, P=20)
out3 = do.llle(X, bandwidth=0.9, P=20)
# visualize the results
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=label, main="bandwidth=0.1")
plot(out2$Y, col=label, main="bandwidth=0.5")
plot(out3$Y, col=label, main="bandwidth=0.9")
par(opar)
}