Journal Articles Electronic Journal of Statistics Year : 2020

Gaussian field on the symmetric group: Prediction and learning

Abstract

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.
Fichier principal
Vignette du fichier
permutations_final.pdf (546.57 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01731251 , version 1 (14-03-2018)
hal-01731251 , version 2 (19-07-2018)
hal-01731251 , version 3 (09-09-2018)
hal-01731251 , version 4 (19-04-2019)
hal-01731251 , version 5 (05-02-2020)

Identifiers

Cite

François Bachoc, Baptiste Broto, Fabrice Gamboa, Jean-Michel Loubes. Gaussian field on the symmetric group: Prediction and learning. Electronic Journal of Statistics , 2020, 14, pp.503-546. ⟨10.1214/19-EJS1674⟩. ⟨hal-01731251v5⟩
368 View
375 Download

Altmetric

Share

More