Rdimtools (version 1.0.0)

est.twonn: Intrinsic Dimension Estimation by a Minimal Neighborhood Information

Description

Unlike many intrinsic dimension (ID) estimation methods, est.twonn only requires two nearest datapoints from a target point and their distances. This extremely minimal approach is claimed to redue the effects of curvature and density variation across different locations in an underlying manifold.

Usage

est.twonn(X)

Arguments

X

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

Value

a named list containing containing

estdim

estimated intrinsic dimension.

References

facco_estimating_2017Rdimtools

Examples

# NOT RUN {
## create 3 datasets of intrinsic dimension 2.
X1 = aux.gensamples(dname="swiss")
X2 = aux.gensamples(dname="ribbon")
X3 = aux.gensamples(dname="saddle")

## acquire an estimate for intrinsic dimension
out1 = est.twonn(X1)
out2 = est.twonn(X2)
out3 = est.twonn(X3)

## print the results
sprintf("* est.twonn : estimated dimension for 'swiss'  data is %.2f.",out1$estdim)
sprintf("* est.twonn : estimated dimension for 'ribbon' data is %.2f.",out2$estdim)
sprintf("* est.twonn : estimated dimension for 'saddle' data is %.2f.",out3$estdim)
# }
# NOT RUN {
# }