Probabilistic learning on manifolds

The probabilistic learning on manifolds (PLoM) is a machine learning technique, proposed by Christian Soize and Roger Ghanem^[1], to construct learned datasets from a given small dataset.

References

1. Data-driven probability concentration and sampling on manifold