Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space. In a number of applications, a priori known spatial constraints can shrink the ambient space into a considerably smaller manifold. Additionally, in these applications the Euclidean...

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Hauptverfasser:	Mallasto, Anton, Hauberg, Søren, Feragen, Aasa
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
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