nonlinear_SAMMON: Sammon Mapping

In Rdimtools: Dimension Reduction and Estimation Methods

Sammon Mapping

Description

do.sammon is an implementation for Sammon mapping, one of the earliest dimension reduction techniques that aims to find low-dimensional embedding that preserves pairwise distance structure in high-dimensional data space.

Usage

do.sammon(
  X,
  ndim = 2,
  preprocess = c("null", "center", "scale", "cscale", "decorrelate", "whiten"),
  initialize = c("pca", "random")
)

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.
initialize	"random" or "pca"; the former performs fast random projection (see also do.rndproj) and the latter performs standard PCA (see also do.pca).

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.

Author(s)

Kisung You

References

Sammon, J.W. (1969) A Nonlinear Mapping for Data Structure Analysis. IEEE Transactions on Computers, C-18 5:401-409.

\insertRef

sammon_nonlinear_1969Rdimtools

Examples

## load iris data
data(iris)
X     = as.matrix(iris[,1:4])
label = as.factor(iris$Species)

## compare two initialization
out1 = do.sammon(X,ndim=2)                   # random projection
out2 = do.sammon(X,ndim=2,initialize="pca")  # pca as initialization

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, pch=19, col=label, main="out1:rndproj")
plot(out2$Y, pch=19, col=label, main="out2:pca")
par(opar)

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