DANCo: Dimension Estimation With the DANCo and MIND Methods
In intrinsicDimension: Intrinsic Dimension Estimation

Description Usage Arguments Details Value Author(s) References Examples

Intrinsic dimension estimation with the DANCo (Ceruti et al. 2012), MIND_MLi and MIND_MLk (Rozza et al. 2012) methods.

1	dancoDimEst(data, k, D, ver = "DANCo", calibration.data = NULL)

`data`	a data set for which the intrinsic dimension is estimated.
`k`	neighborhood parameter.
`D`	maximal dimension.
`ver`	possible values: 'DANCo', 'MIND_MLi', 'MIND_MLk'.
`calibration.data`	precomputed calibration data.

If cal = NULL or the cal$maxdim < D new calibration data will be computed as needed.

A DimEst object with slots:

`dim.est`	the intrinsic dimension estimate.
`kl.divergence`	the KL divergence between data and reference data for the estimated dimension (if ver == 'DANCo').
`calibration.data`	calibration data that can be reused when applying DANCo to data sets of the same size with the same neighborhood parameter k.

Kerstin Johnsson, Lund University

Ceruti, C. et al. (2012) DANCo: Dimensionality from Angle and Norm Concentration. arXiv preprint 1206.3881.

Rozza, A et al. (2012) Novel high intrinsic dimensionality estimators. Machine learning 89, 37-65.

data <- hyperBall(50, 10)
res <- dancoDimEst(data, 8, 20)
print(res)

## Reusing calibration data
data2 <- hyperBall(50, 5)
dancoDimEst(data2, 8, 20, calibration.data=res$calibration.data)