linear_LMDS: Landmark Multidimensional Scaling

In Rdimtools: Dimension Reduction and Estimation Methods

Landmark Multidimensional Scaling

Description

Landmark MDS is a variant of Classical Multidimensional Scaling 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.

Usage

do.lmds(X, ndim = 2, npoints = max(nrow(X)/5, ndim + 1))

Arguments

X	an (n\times p) matrix whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension.
npoints	the number of landmark points to be drawn.

Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

projection

a (p\times ndim) whose columns are basis for projection.

algorithm

name of the algorithm.

Author(s)

Kisung You

References

\insertRef

silva_global_2002Rdimtools

\insertRef

lee_landmark_2009Rdimtools

See Also

do.mds

Examples

## use iris data
data(iris)
X     = as.matrix(iris[,1:4])
lab   = as.factor(iris[,5])

## use 10% and 25% of the data and compare with full MDS
output1 <- do.lmds(X, ndim=2, npoints=round(nrow(X)*0.10))
output2 <- do.lmds(X, ndim=2, npoints=round(nrow(X)*0.25))
output3 <- do.mds(X, ndim=2)

## vsualization
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(output1$Y, pch=19, col=lab, main="10% random points")
plot(output2$Y, pch=19, col=lab, main="25% random points")
plot(output3$Y, pch=19, col=lab, main="original MDS")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.