Search Results for author: Laure Ferraris

Found 1 papers, 0 papers with code

Choosing the parameter of the Fermat distance: navigating geometry and noise

no code implementations30 Nov 2023 Frédéric Chazal, Laure Ferraris, Pablo Groisman, Matthieu Jonckheere, Frédéric Pascal, Facundo Sapienza

The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve the results given by Euclidean distances by exploding the geometrical and statistical properties of the dataset.

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