Gaussian Processes indexed on the symmetric group: prediction and learning
Machine Learning
2020-02-14 v4
Abstract
In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X. The Euclidean case 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. In this setting, we propose and study an harmonic analysis of the covariance operators that enables to consider Gaussian processes models and forecasting issues. Our theory is motivated by statistical ranking problems.
Keywords
Cite
@article{arxiv.1803.06118,
title = {Gaussian Processes indexed on the symmetric group: prediction and learning},
author = {François Bachoc and Baptiste Broto and Fabrice Gamboa and Jean-Michel Loubes},
journal= {arXiv preprint arXiv:1803.06118},
year = {2020}
}