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Medientyp:
E-Artikel
Titel:
Probabilistic learning on manifolds (PLoM) with partition
Beteiligte:
Soize, Christian;
Ghanem, Roger
Erschienen:
Wiley, 2022
Erschienen in:International Journal for Numerical Methods in Engineering
Sprache:
Englisch
DOI:
10.1002/nme.6856
ISSN:
0029-5981;
1097-0207
Entstehung:
Anmerkungen:
Beschreibung:
<jats:title>Abstract</jats:title><jats:p>The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number<jats:italic>N</jats:italic>of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which<jats:italic>N</jats:italic>is very small and for which the dimension of the diffusion‐map basis is close to<jats:italic>N</jats:italic>. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion‐map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.</jats:p>