Intrinsic Dimension Estimation
Add Noise to Data Set
Turn a local estimator into a pointwise estimator
Dimension Estimation With Optimally Topology Preserving Maps
Corner Plane
Piece of Noisy Hyperplane
Piece of Noisy Hypersphere
Dimension Estimation With the DANCo and MIND Methods
Expected Simplex Skewness Local Dimension Estimation
ESS Reference Values
Hypercube
Intrinsic Dimension Estimation
Intrinsic Dimension Estimation and Data on Manifolds
Dimension Estimation from kNN Distances
Local Dimension Estimation with PCA
12-dimensional manifold from Hein and Audibert (2005)
Manifolds from Rozza et al. (2012)
Dimension Estimation via Translated Poisson Distributions
Obtaining neighborhoods (local data) from a data set
Transition Functions Describing Noise
Oblong Normal Distribution
Isotropic Distributions With or Without Noise
Swiss roll with or without 3-sphere inside
Twin Peaks
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.