Probabilistic learning on manifolds: Difference between revisions

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#REDIRECT [[Nonlinear dimensionality reduction]]

The '''probabilistic learning on manifolds''' (PLoM) is a [[machine learning]] technique, proposed by [[Christian Soize]] and [[Roger Ghanem]]<ref>[https://www.sciencedirect.com/science/article/pii/S0021999116301899 Data-driven probability concentration and sampling on manifold]</ref>, to construct learned datasets from a given small dataset.

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== References ==

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[[Category:Applied mathematics]]

[[Category:Probability theory]]

[[Category:Statistical theory]]

[[Category:Machine learning]]