linear_ASI: Adaptive Subspace Iteration

In Rdimtools: Dimension Reduction and Estimation Methods

Adaptive Subspace Iteration

Description

Adaptive Subspace Iteration (ASI) 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 within-cluster scatter matrix are used as basis of projection.

Usage

do.asi(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.

algorithm

name of the algorithm.

Author(s)

Kisung You

References

\insertRef

li_document_2004Rdimtools

See Also

do.ldakm

Examples

## use iris data
data(iris, package="Rdimtools")
set.seed(100)
subid = sample(1:150, 50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## compare ASI with other methods
outASI = do.asi(X)
outPCA = do.pca(X)
outLDA = do.lda(X, label)

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

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