Gaussian field on the symmetric group: Prediction and learning
Résumé
In the framework of the supervised learning of a real function defined on an abstract space X, Gaussian processes are widely used. The Euclidean case for X is well known and has been widely studied. In this paper, we explore the less classical case where X is the non commutative finite group of permutations (namely the so-called symmetric group SN). We provide an application to Gaussian process based optimization of Latin Hypercube Designs. We also extend our results to the case of partial rankings.
Domaines
Machine Learning [stat.ML]| Origine | Fichiers produits par l'(les) auteur(s) |
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