Intrinsic dimensionality estimation techniques
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/
Citation pour cette source
Gabriele Lombardi (2026). Intrinsic dimensionality estimation techniques (https://fr.mathworks.com/matlabcentral/fileexchange/40112-intrinsic-dimensionality-estimation-techniques), MATLAB Central File Exchange. Extrait(e) le .
Compatibilité avec les versions de MATLAB
Plateformes compatibles
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- AI and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
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Remerciements
A inspiré : Rand Sphere.zip
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| Version | Publié le | Notes de version | |
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| 1.1.0.0 | Added a reference to an R implementation in the description. |
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| 1.0.0.0 |
