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Selection of variables structured by regularization in a multi-task framework

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.
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Submitted on : Thursday, June 1, 2017 - 9:14:29 AM
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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⟩

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