geomstats: a Python Package for Riemannian Geometry in Machine Learning

Abstract : We introduce geomstats, a python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups of transformations. We provide efficient and extensively unit-tested implementations of these manifolds, together with useful Riemannian metrics and associated Exponential and Logarithm maps. The corresponding geodesic distances provide a range of intuitive choices of Machine Learning loss functions. We also give the corresponding Riemannian gradients. The operations implemented in geomstats are available with different computing backends such as numpy, tensorflow and keras. We have enabled GPU implementation and integrated geomstats manifold computations into keras deep learning framework. This paper also presents a review of manifolds in machine learning and an overview of the geomstats package with examples demonstrating its use for efficient and user-friendly Riemannian geometry.
Type de document :
Pré-publication, Document de travail
Preprint NIPS2018. 2019
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Contributeur : Xavier Pennec <>
Soumis le : mardi 8 janvier 2019 - 19:12:43
Dernière modification le : mardi 19 février 2019 - 01:13:50

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  • HAL Id : hal-01974572, version 1
  • ARXIV : 1805.08308



Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec. geomstats: a Python Package for Riemannian Geometry in Machine Learning. Preprint NIPS2018. 2019. 〈hal-01974572〉



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