Binarsity: a penalization for one-hot encoded features in linear supervised learning

24 Mar 2017Mokhtar Z. AlayaSimon BussyStéphane GaïffasAgathe Guilloux

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 \emph{binarsity}... (read more)

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