Rdimtools (version 0.3.2)

do.sammon: 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("random", "pca"))

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.

References

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

sammon_nonlinear_1969Rdimtools

Examples

# NOT RUN {
## generate default dataset
## in order to pass CRAN pretest, n is set to be small.
X <- aux.gensamples(n=99)

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

par(mfrow=c(1,2))
plot(out1$Y[,1],out1$Y[,2],main="out1:rndproj")
plot(out2$Y[,1],out2$Y[,2],main="out2:pca")
# }
# NOT RUN {
# }