ESS: Expected Simplex Skewness Local Dimension Estimation

In intrinsicDimension: Intrinsic Dimension Estimation

Description

Local intrinsic dimension estimation with the ESS method

Usage

essLocalDimEst(data, ver, d = 1)

Arguments

data	Local data set for which dimension should be estimated.
ver	Possible values: 'a' and 'b'. See Johnsson et al. (2015).
d	For ver = 'a', any value of d is possible, for ver = 'b', only d = 1 is supported.

Details

The ESS method assumes that the data is local, i.e. that it is a neighborhood taken from a larger data set, such that the curvature and the noise within the neighborhood is relatively small. In the ideal case (no noise, no curvature) this is equivalent to the data being uniformly distributed over a hyper ball.

Value

A DimEst object with two slots:

dim.est	The interpolated dimension estimate.
ess	The ESS value produced by the algorithm.

Author(s)

Kerstin Johnsson, Lund University

References

Johnsson, K., Soneson, C., & Fontes, M. (2015). Low Bias Local Intrinsic Dimension Estimation from Expected Simplex Skewness. IEEE Trans. Pattern Anal. Mach. Intell., 37(1), 196-202.

Examples

data <- hyperBall(100, 4, 15, .05)
essLocalDimEst(data, ver = 'a', d = 1)

intrinsicDimension documentation built on June 7, 2019, 5:02 p.m.