Intrinsic dimensionality estimation techniques

Version 1.1.0.0 (229 ko) par Gabriele Lombardi
Implementation of some state-of-art intrinsic dimensionality estimators.
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Mise à jour 24 mai 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/

Citation pour cette source

Gabriele Lombardi (2024). Intrinsic dimensionality estimation techniques (https://www.mathworks.com/matlabcentral/fileexchange/40112-intrinsic-dimensionality-estimation-techniques), MATLAB Central File Exchange. Récupéré le .

Compatibilité avec les versions de MATLAB
Créé avec R2011b
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En savoir plus sur Dimensionality Reduction and Feature Extraction dans Help Center et MATLAB Answers
Remerciements

A inspiré : Rand Sphere.zip

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idEstimation/

idEstimation/private/

idEstimation/html/

Version Publié le Notes de version
1.1.0.0

Added a reference to an R implementation in the description.
1.0.0.0