do.lapeig
performs Laplacian Eigenmaps (LE) to discover low-dimensional
manifold embedded in high-dimensional data space using graph laplacians. This
is a classic algorithm employing spectral graph theory.
do.lapeig(X, ndim = 2, ...)
an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.
an integer-valued target dimension.
extra parameters including
kernel scale parameter. Default value is 1.0.
an additional option for preprocessing the data.
Default is "null"
. See also aux.preprocess
for more details.
one of "intersect"
, "union"
or "asymmetric"
is supported. Default is "union"
. See also aux.graphnbd
for more details.
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.
a logical; TRUE
for weighted graph laplacian and FALSE
for
combinatorial laplacian where connectivity is represented as 1 or 0 only.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a vector of eigenvalues for laplacian matrix.
a list containing information for out-of-sample prediction.
name of the algorithm.
Belkin M, Niyogi P (2003). “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation.” Neural Computation, 15(6), 1373--1396.
# \donttest{
## use iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X = as.matrix(iris[subid,1:4])
lab = as.factor(iris[subid,5])
## try different levels of connectivity
out1 <- do.lapeig(X, type=c("proportion",0.5), weighted=FALSE)
out2 <- do.lapeig(X, type=c("proportion",0.10), weighted=FALSE)
out3 <- do.lapeig(X, type=c("proportion",0.25), weighted=FALSE)
## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=lab, main="5% connected")
plot(out2$Y, pch=19, col=lab, main="10% connected")
plot(out3$Y, pch=19, col=lab, main="25% connected")
par(opar)
# }