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Communication dans un congrès

Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning

Vivien Cabannes 1 Loucas Pillaud-Vivien 2, 3 Francis Bach 1 Alessandro Rudi 1
1 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique - ENS Paris, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : As annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning. This is the aim of semi-supervised learning. To benefit from the access to unlabelled data, it is natural to diffuse smoothly knowledge of labelled data to unlabelled one. This induces to the use of Laplacian regularization. Yet, current implementations of Laplacian regularization suffer from several drawbacks, notably the well-known curse of dimensionality. In this paper, we provide a statistical analysis to overcome those issues, and unveil a large body of spectral filtering methods that exhibit desirable behaviors. They are implemented through (reproducing) kernel methods, for which we provide realistic computational guidelines in order to make our method usable with large amounts of data.
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https://hal.archives-ouvertes.fr/hal-03454809
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Soumis le : lundi 29 novembre 2021 - 13:33:40
Dernière modification le : vendredi 21 janvier 2022 - 03:23:01

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  • HAL Id : hal-03454809, version 1

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Vivien Cabannes, Loucas Pillaud-Vivien, Francis Bach, Alessandro Rudi. Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. NeurIPS 2021 - Thirty-fifth conference on Neural Information Processing Systems (NeurIPS), Dec 2021, Online, Unknown Region. ⟨hal-03454809⟩

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