do.lisomap | R Documentation |
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.
do.lisomap( X, ndim = 2, ltype = c("random", "MaxMin"), npoints = max(nrow(X)/5, ndim + 1), preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"), type = c("proportion", 0.1), symmetric = c("union", "intersect", "asymmetric"), weight = TRUE )
X |
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. |
ndim |
an integer-valued target dimension. |
ltype |
on how to select landmark points, either |
npoints |
the number of landmark points to be drawn. |
preprocess |
an option for preprocessing the data. Default is "center". See also |
type |
a vector of neighborhood graph construction. Following types are supported;
|
symmetric |
one of |
weight |
|
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
Kisung You
silva_global_2003Rdimtools
do.isomap
## 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)
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