Geodesic
Machine learning and optimal transport: some statistical and algorithmic tools
Elsa Cazelles1
1 CNRS, IRIT, Université de Toulouse
ESAIM. Proceedings, Tome 74 (2023), pp. 158-168.

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In this paper, we focus on the analysis of data that can be described by probability measures supported on a Euclidean space, by way of optimal transport. Our main objective is to present a first and second order statistical analyses in the space of distributions in a concise manner, as a first approach to understand the general modes of variation of a set of observations. In the context of optimal transport, these studies correspond to the barycenter and the decomposition into geodesic principal components in theWasserstein space. In particular, we aim attention at a regularised estimator of the barycenter, in order to handle the noise coming from the observations. Additionally, we leverage these tools for time series analysis, whose spectral informations are compared using optimal transport.
DOI : 10.1051/proc/202374158

Elsa Cazelles 1

1 CNRS, IRIT, Université de Toulouse 
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Elsa Cazelles. Machine learning and optimal transport: some statistical and algorithmic tools. ESAIM. Proceedings, Tome 74 (2023), pp. 158-168. doi : 10.1051/proc/202374158. http://geodesic.mathdoc.fr/articles/10.1051/proc/202374158/

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