Tuesday, 18th of May

14h30 (room R2014, 660 building) (see location)

Guillaume Doquet


Agnostic Feature Selection / Sélection d'attributs agnostique


Unsupervised feature selection is mostly assessed along a
supervised learning setting, depending on whether the selected features
efficiently permit to predict the (unknown) target variable. Another
setting is proposed in this work : the selected features aim to
efficiently recover the whole dataset. The proposed algorithm, called
AgnoS, combines an auto-encoder with structural regularizations to
sidestep the combinatorial optimization problem at the core of feature
selection. The extensive experimental validation of AgnoS on the
scikit-feature benchmark suite demonstrates its ability compared to the
state of the art, both in terms of supervised learning and data

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Contact: guillaume.charpiat at

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Page dernièrement modifiée le Lundi 17 juin 2019 16:15:33 CEST par guillaume.