do.lpp {Rdimtools} | R Documentation |
Locality Preserving Projection
Description
do.lpp
is a linear approximation to Laplacian Eigenmaps. More precisely,
it aims at finding a linear approximation to the eigenfunctions of the Laplace-Beltrami
operator on the graph-approximated data manifold.
Usage
do.lpp(
X,
ndim = 2,
type = c("proportion", 0.1),
symmetric = c("union", "intersect", "asymmetric"),
preprocess = c("center", "scale", "cscale", "whiten", "decorrelate"),
t = 1
)
Arguments
X |
an |
ndim |
an integer-valued target dimension. |
type |
a vector of neighborhood graph construction. Following types are supported;
|
symmetric |
one of |
preprocess |
an additional option for preprocessing the data.
Default is |
t |
bandwidth for heat kernel in |
Value
a named list containing
- Y
an
(n\times ndim)
matrix whose rows are embedded observations.- projection
a
(p\times ndim)
whose columns are basis for projection.- trfinfo
a list containing information for out-of-sample prediction.
Author(s)
Kisung You
References
He X (2005). Locality Preserving Projections. PhD Thesis, University of Chicago, Chicago, IL, USA.
Examples
## use iris dataset
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 kernel bandwidths
out1 <- do.lpp(X, t=0.1)
out2 <- do.lpp(X, t=1)
out3 <- do.lpp(X, t=10)
## Visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=lab, pch=19, main="LPP::bandwidth=0.1")
plot(out2$Y, col=lab, pch=19, main="LPP::bandwidth=1")
plot(out3$Y, col=lab, pch=19, main="LPP::bandwidth=10")
par(opar)