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Rdimtools (version 1.0.0)

do.ree: Robust Euclidean Embedding

Description

Robust Euclidean Embedding (REE) is an embedding procedure exploiting robustness of \(\ell_1\) cost function. In our implementation, we adopted a generalized version with weight matrix to be applied as well. Its original paper introduced a subgradient algorithm to overcome memory-intensive nature of original semidefinite programming formulation.

Usage

do.ree(
  X,
  ndim = 2,
  W = NA,
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"),
  initc = 1,
  dmethod = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  maxiter = 100,
  abstol = 0.001
)

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.

W

an \((n\times n)\) weight matrix. Default is uniform weight of 1s.

preprocess

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

initc

initial c value for subgradient iterating stepsize, \(c/\sqrt{i}\).

dmethod

a type of distance measure. See dist for more details.

maxiter

maximum number of iterations for subgradient descent method.

abstol

stopping criterion for subgradient descent method.

Value

a named list containing

Y

an \((n\times ndim)\) matrix whose rows are embedded observations.

niter

the number of iterations taken til convergence.

trfinfo

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

References

cayton_robust_2006Rdimtools

Examples

Run this code
# NOT RUN {
## use iris data
data(iris)
X     = as.matrix(iris[,1:4])
label = as.integer(iris$Species)

## 1. no preprocessing
output1 <- do.ree(X,ndim=2,maxiter=50)

## 2. use decorrelated data
output2 <- do.ree(X,ndim=2,preprocess="decorrelate",maxiter=50)

## Visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(output1$Y, col=label, main="centered")
plot(output2$Y, col=label, main="decorrelated")
par(opar)
# }
# NOT RUN {
# }

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