Geomstats: A Python Package for Riemannian Geometry in Machine Learning
GUIGUI, Nicolas
Université Côte d'Azur [UniCA]
E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
Université Côte d'Azur [UniCA]
E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
LE BRIGANT, Alice
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
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Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
GUIGUI, Nicolas
Université Côte d'Azur [UniCA]
E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
Université Côte d'Azur [UniCA]
E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
LE BRIGANT, Alice
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
PENNEC, Xavier
Université Côte d'Azur [UniCA]
E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
< Reduce
Université Côte d'Azur [UniCA]
E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
Language
en
Article de revue
This item was published in
Journal of Machine Learning Research. 2020-12-20, vol. 21, n° 223, p. 1-9
Microtome Publishing
English Abstract
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and ...Read more >
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai.Read less <
English Keywords
differential geometry
Riemannian geometry
statistics
machine learning
manifold
European Project
G-Statistics - Foundations of Geometric Statistics and Their Application in the Life Sciences
ANR Project
Idex UCA JEDI - ANR-15-IDEX-0001
3IA Côte d'Azur - ANR-19-P3IA-0002
3IA Côte d'Azur - ANR-19-P3IA-0002
Origin
Hal imported