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Binarsity: a penalization for one-hot encoded features

Abstract : This paper deals with the problem of large-scale linear supervised learning in settings where a large number of continuous features are available. We propose to combine the well-known trick of one-hot encoding of continuous features with a new penalization called binarsity. In each group of binary features coming from the one-hot encoding of a single raw continuous feature, this penalization uses total-variation regularization together with an extra linear constraint to avoid collinearity within groups. Non-asymptotic oracle inequalities for generalized linear models are proposed, and numerical experiments illustrate the good performances of our approach on several datasets. It is also noteworthy that our method has a numerical complexity comparable to standard L1 penalization.
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Preprints, Working Papers, ...
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Contributor : Simon Bussy <>
Submitted on : Saturday, November 25, 2017 - 5:14:25 PM
Last modification on : Tuesday, March 17, 2020 - 3:24:59 AM


  • HAL Id : hal-01648382, version 1


Mokhtar Z. Alaya, Simon Bussy, Stéphane Gaïffas, Agathe Guilloux. Binarsity: a penalization for one-hot encoded features. 2017. ⟨hal-01648382⟩



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