essLocalDimEst {intrinsicDimension} | R Documentation |
Expected Simplex Skewness Local 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: |
d |
For |
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)