estimate_made: Manifold-Adaptive Dimension Estimation

In kisungyou/Rdimtools: Dimension Reduction and Estimation Methods

Manifold-Adaptive Dimension Estimation

Description

do.made first aims at finding local dimesion estimates using nearest neighbor techniques based on the first-order approximation of the probability mass function and then combines them to get a single global estimate. Due to the rate of convergence of such estimate to be independent of assumed dimensionality, authors claim this method to be manifold-adaptive.

Usage

est.made(
  X,
  k = round(sqrt(ncol(X))),
  maxdim = min(ncol(X), 15),
  combine = c("mean", "median", "vote")
)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations.
k	size of neighborhood for analysis.
maxdim	maximum possible dimension allowed for the algorithm to investigate.
combine	method to aggregate local estimates for a single global estimate.

Value

a named list containing containing

estdim

estimated global intrinsic dimension.

estloc

a length-n vector estimated dimension at each point.

Author(s)

Kisung You

References

\insertRef

farahmand_manifoldadaptive_2007Rdimtools

Examples

## create a data set of intrinsic dimension 2.
X = aux.gensamples(dname="swiss")

## compare effect of 3 combining scheme
out1 = est.made(X, combine="mean")
out2 = est.made(X, combine="median")
out3 = est.made(X, combine="vote")

## print the results
line1 = paste0("* est.made : 'mean'   estiamte is ",round(out1$estdim,2))
line2 = paste0("* est.made : 'median' estiamte is ",round(out2$estdim,2))
line3 = paste0("* est.made : 'vote'   estiamte is ",round(out3$estdim,2))
cat(paste0(line1,"\n",line2,"\n",line3))

kisungyou/Rdimtools documentation built on Jan. 2, 2023, 9:55 a.m.