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lmds

lmds: Landmark Multi-Dimensional Scaling

A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.

library(lmds)
x <- as.matrix(iris[,1:4])
dimred <- lmds(x, ndim = 2)
qplot(dimred[,1], dimred[,2]) + labs(title = "lmds()") + theme_classic()

dimred <- cmdscale(dist(x))
qplot(dimred[,1], dimred[,2]) + labs(title = "cmdscale()") + theme_classic()

Execution time

The execution time of lmds() scales linearly with respect to the dataset size.

Latest changes

Check out news(package = "lmds") or NEWS.md for a full list of changes.

Recent changes in lmds 0.1.0

Initial release of lmds.

• A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.