| 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)