Landmark Isomap is a variant of Isomap in that it first finds a low-dimensional embedding using a small portion of given dataset and graft the others in a manner to preserve as much pairwise distance from all the other data points to landmark points as possible.
an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.
an integer-valued target dimension.
on how to select landmark points, either "random"
or "MaxMin"
.
the number of landmark points to be drawn.
an option for preprocessing the data. Default is "center". See also aux.preprocess
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.
one of "intersect"
, "union"
or "asymmetric"
is supported. Default is "union"
. See also aux.graphnbd
for more details.
TRUE
to perform Landmark Isomap on weighted graph, or FALSE
otherwise.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
Silva VD, Tenenbaum JB (2003). “Global Versus Local Methods in Nonlinear Dimensionality Reduction.” In Becker S, Thrun S, Obermayer K (eds.), Advances in Neural Information Processing Systems 15, 721--728. MIT Press.
# \donttest{
## use iris data
data(iris)
X <- as.matrix(iris[,1:4])
lab <- as.factor(iris[,5])
## use different number of data points as landmarks
output1 <- do.lisomap(X, npoints=10, type=c("proportion",0.25))
output2 <- do.lisomap(X, npoints=25, type=c("proportion",0.25))
output3 <- do.lisomap(X, npoints=50, type=c("proportion",0.25))
## visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(output1$Y, pch=19, col=lab, main="10 landmarks")
plot(output2$Y, pch=19, col=lab, main="25 landmarks")
plot(output3$Y, pch=19, col=lab, main="50 landmarks")
par(opar)
# }