Probabilistic learning on manifolds: Difference between revisions

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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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|concern = No assertion of notability, only citation is to a non-independent recent research paper (almost certainly too recent to have established meaningful impact or notability).

|timestamp = 20210601202642

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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.

== References ==