do.isomap
is an efficient implementation of a well-known Isomap method
by Tenenbaum et al (2000). Its novelty comes from applying classical multidimensional
scaling on nonlinear manifold, which is approximated as a graph.
do.isomap(
X,
ndim = 2,
type = c("proportion", 0.1),
symmetric = c("union", "intersect", "asymmetric"),
weight = TRUE,
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)
an
an integer-valued target dimension.
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 Isomap on weighted graph, or FALSE
otherwise.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
a named list containing
an
a list containing information for out-of-sample prediction.
silva_global_2003Rdimtools
# NOT RUN {
## generate data
X <- aux.gensamples(n=123)
## 1. connecting 10% of data for graph construction.
output1 <- do.isomap(X,ndim=2,type=c("proportion",0.10))
## 2. constructing 25%-connected graph
output2 <- do.isomap(X,ndim=2,type=c("proportion",0.25))
## 3. constructing 25%-connected with binarization
output3 <- do.isomap(X,ndim=2,type=c("proportion",0.25),weight=FALSE)
## Visualize three different projections
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(output1$Y, main="10%")
plot(output2$Y, main="25%")
plot(output3$Y, main="25%+Binary")
par(opar)
# }
# NOT RUN {
# }
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