Intrinsic dimensionality estimation techniques

Implementation of some state-of-art intrinsic dimensionality estimators.
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Updated 24 May 2013

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Data analysis is a fundamental step to face real Machine-Learning problems, various well-known ML techniques, such as those related to clustering or dimensionality reduction, require the intrinsic dimensionality (id) of the dataset as a parameter.

To the aim of automate the estimation of the id, in literature various techniques has been described, this small toolbox contains the implementation of some state-of-art of them, that is: MLE, MiND_ML, MiND_KL, DANCo, DANCoFit.

For an R implementation see:
http://www.maths.lth.se/matematiklth/personal/johnsson/dimest/

Cite As

Gabriele Lombardi (2025). Intrinsic dimensionality estimation techniques (https://www.mathworks.com/matlabcentral/fileexchange/40112-intrinsic-dimensionality-estimation-techniques), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2011b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Categories
Find more on Dimensionality Reduction and Feature Extraction in Help Center and MATLAB Answers
Acknowledgements

Inspired: Rand Sphere.zip

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Version Published Release Notes
1.1.0.0

Added a reference to an R implementation in the description.

1.0.0.0