ECM - École Centrale de Marseille : UMR7279 (Pôle de l'étoile - Technopole de Château-Gombert - 38 rue Frédéric Joliot-Curie - 13013 Marseille - France)
Abstract : Motivated by diagnostic applications in the field of clinical microbiology, we introduce a joint in-put/output regularization method to perform struc-tured variable selection in a multi-task setting where tasks can exhibit various degrees of correlation. Our approach extensively relies on the tree-structured group-lasso penalty and explicitly combines hierarchical structures defined across features and task by means of the Cartesian product of graphs to induce a global hierarchical group structure. A vectorization procedure is then used to solve the resulting multi-task problem with standard mono-task optimization algorithms developed for the overlapping group-lasso problem. Experimental results on simulated and real data demonstrate the interest of the approach.
https://hal-amu.archives-ouvertes.fr/hal-01373190
Contributor : Valentin Emiya
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Submitted on : Thursday, June 1, 2017 - 9:14:29 AM
Last modification on : Thursday, January 23, 2020 - 6:22:14 PM
Long-term archiving on: Wednesday, September 6, 2017 - 6:12:04 PM
Antoine Bonnefoy, Ismael Ouamlil, Jean-Baptiste Veyrieras, Pierre Mahé. Selection of variables structured by regularization in a multi-task framework. Conférence francophone sur l’apprentissage automatique (CAp), Jul 2016, Marseille, France. ⟨hal-01373190⟩
