Rdimtools (version 1.1.2)

do.ldakm: Combination of LDA and K-means

Description

do.ldakm is an unsupervised subspace discovery method that combines linear discriminant analysis (LDA) and K-means algorithm. It tries to build an adaptive framework that selects the most discriminative subspace. It iteratively applies two methods in that the clustering process is integrated with the subspace selection, and continuously updates its discrimative basis. From its formulation with respect to generalized eigenvalue problem, it can be considered as generalization of Adaptive Subspace Iteration (ASI) and Adaptive Dimension Reduction (ADR).

Usage

do.ldakm(
  X,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  maxiter = 10,
  abstol = 0.001
)

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.

projection

a \((p\times ndim)\) whose columns are basis for projection.

Arguments

X

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

ndim

an integer-valued target dimension.

preprocess

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

maxiter

maximum number of iterations allowed.

abstol

stopping criterion for incremental change in projection matrix.

Author

Kisung You

References

ding_adaptive_2007Rdimtools

See Also

do.asi, do.adr

Examples

## use iris dataset
data(iris)
set.seed(100)
subid <- sample(1:150, 50)
X     <- as.matrix(iris[subid,1:4])
lab   <- as.factor(iris[subid,5])

## try different tolerance level
out1 = do.ldakm(X, abstol=1e-2)
out2 = do.ldakm(X, abstol=1e-3)
out3 = do.ldakm(X, abstol=1e-4)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=lab, main="LDA-KM::tol=1e-2")
plot(out2$Y, pch=19, col=lab, main="LDA-KM::tol=1e-3")
plot(out3$Y, pch=19, col=lab, main="LDA-KM::tol=1e-4")
par(opar)