# nonlinear_REE: Robust Euclidean Embedding In Rdimtools: Dimension Reduction and Estimation Methods

 do.ree R Documentation

## 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/√{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.

Kisung You

### References

\insertRef

cayton_robust_2006Rdimtools

### Examples


## use 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])

## try different distance method
output1 <- do.ree(X, maxiter=50, dmethod="euclidean")
output2 <- do.ree(X, maxiter=50, dmethod="maximum")
output3 <- do.ree(X, maxiter=50, dmethod="canberra")

## visualize three different projections
plot(output1$Y, col=label, pch=19, main="dmethod-euclidean") plot(output2$Y, col=label, pch=19, main="dmethod-maximum")