Rdimtools (version 1.0.4)

do.llle: Local Linear Laplacian Eigenmaps

Description

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.

Usage

do.llle(
  X,
  ndim = 2,
  preprocess = c("null", "center", "scale", "cscale", "decorrelate", "whiten"),
  K = round(nrow(X)/2),
  P = max(round(nrow(X)/4), 2),
  bandwidth = 0.2
)

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.

preprocess

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

K

size of near neighborhood for each data point.

P

size of artifical neighborhood.

bandwidth

scale parameter for Gaussian kernel. It should be in \((0,1)\).

Value

a named list containing

Y

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

trfinfo

a list containing information for out-of-sample prediction.

References

liu_local_2016Rdimtools

See Also

do.lle

Examples

# NOT RUN {
## 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)
# }
# NOT RUN {
# }