Rdimtools (version 0.4.1)

do.nnp: Nearest Neighbor Projection

Description

Nearest Neighbor Projection is an iterative method for visualizing high-dimensional dataset in that a data is sequentially located in the low-dimensional space by maintaining the triangular distance spread of target data with its two nearest neighbors in the high-dimensional space. We extended the original method to be applied for arbitrarily low-dimensional space. Due the generalization, we opted for a global optimization method of Differential Evolution (DEoptim) within in that it may add computational burden to certain degrees.

Usage

do.nnp(X, ndim = 2, preprocess = c("null", "center", "scale", "cscale",
  "whiten", "decorrelate"))

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.

preprocess

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

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.

References

tejada_improved_2003Rdimtools

Examples

# NOT RUN {
## load iris data
data(iris)
X <- as.matrix(iris[,1:4])

## let's compare with other methods
out1 <- do.nnp(X, ndim=2)      # NNP
out2 <- do.pca(X, ndim=2)      # PCA
out3 <- do.lamp(X, ndim=2)     # LAMP


## visualize
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="NNP")
plot(out2$Y[,1], out2$Y[,2], main="PCA")
plot(out3$Y[,1], out3$Y[,2], main="LAMP")
# }