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/
Zitieren als
Gabriele Lombardi (2024). Intrinsic dimensionality estimation techniques (https://www.mathworks.com/matlabcentral/fileexchange/40112-intrinsic-dimensionality-estimation-techniques), MATLAB Central File Exchange. Abgerufen .
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
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Inspiriert: Rand Sphere.zip
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Version | Veröffentlicht | Versionshinweise | |
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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 |