Near-Neighbor Information with Bias Correction

Source: R/estimate_nearneighbor2.R

Though similar to est.nearneighbor1, authors of the reference argued that there exists innate bias in the method and proposed a non-iterative algorithm to reflect local distance information under a range of neighborhood sizes.

est.nearneighbor2(X, kmin = 2, kmax = max(3, round(ncol(X)/2)))

Arguments

X

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

kmin

minimum neighborhood size, larger than 1.

kmax

maximum neighborhood size, smaller than \(p\).

Value

a named list containing containing

estdim

estimated intrinsic dimension.

References

Verveer PJ, Duin RPW (1995). “An Evaluation of Intrinsic Dimensionality Estimators.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), 81–86.

Author

Kisung You

Examples

# \donttest{
## create an example data with intrinsic dimension 2
X = cbind(aux.gensamples(dname="swiss"),aux.gensamples(dname="swiss"))

## acquire an estimate for intrinsic dimension
output = est.nearneighbor2(X)
sprintf("* est.nearneighbor2 : estimated dimension is %.2f.",output$estdim)
#> [1] "* est.nearneighbor2 : estimated dimension is 4.46."
# }