do.lpp | R Documentation |
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.
do.lpp( X, ndim = 2, type = c("proportion", 0.1), symmetric = c("union", "intersect", "asymmetric"), preprocess = c("center", "scale", "cscale", "whiten", "decorrelate"), t = 1 )
X |
an (n\times p) matrix or data frame whose rows are observations |
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 (0,∞). |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a (p\times ndim) whose columns are basis for projection.
a list containing information for out-of-sample prediction.
Kisung You
he_locality_2005Rdimtools
## 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)
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