# 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.