FastMap

do.fastmap is an implementation of FastMap algorithm. Though it shares similarities with MDS, it is innately a nonlinear method that makes an iterative update for the projection information using pairwise distance information.

do.fastmap(
  X,
  ndim = 2,
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate")
)

Arguments

X

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

preprocess

an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

Value

a named list containing

Y

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

trfinfo

a list containing information for out-of-sample prediction.

References

Faloutsos C, Lin K (1995). “FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets.” In Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data - SIGMOD '95, 163--174.

Author

Kisung You

Examples

if (FALSE) {
## load iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## let's compare with other methods
out1 <- do.pca(X, ndim=2)      # PCA
out2 <- do.mds(X, ndim=2)      # Classical MDS
out3 <- do.fastmap(X, ndim=2)  # FastMap

## visualize
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="PCA")
plot(out2$Y, pch=19, col=label, main="MDS")
plot(out3$Y, pch=19, col=label, main="FastMap")
par(opar)
}