linear_ADR: Adaptive Dimension Reduction

do.adrR Documentation

Adaptive Dimension Reduction

Description

Adaptive Dimension Reduction \insertCiteding_adaptive_2002Rdimtools iteratively finds the best subspace to perform data clustering. It can be regarded as one of remedies for clustering in high dimensional space. Eigenvectors of a between-cluster scatter matrix are used as basis of projection.

Usage

do.adr(X, ndim = 2, ...)

Arguments

X

an (n\times p) matrix or data frame whose rows are observations.

ndim

an integer-valued target dimension.

...

extra parameters including

maxiter

maximum number of iterations (default: 100).

abstol

absolute tolerance stopping criterion (default: 1e-8).

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.

trfinfo

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

algorithm

name of the algorithm.

References

\insertAllCited

See Also

do.ldakm

Examples


## 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])

## compare ADR with other methods
outADR = do.adr(X)
outPCA = do.pca(X)
outLDA = do.lda(X, label)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(outADR$Y, col=label, pch=19, main="ADR")
plot(outPCA$Y, col=label, pch=19, main="PCA")
plot(outLDA$Y, col=label, pch=19, main="LDA")
par(opar)



Rdimtools documentation built on Sept. 23, 2022, 1:06 a.m.