Selection of variables structured by regularization in a multi-task framework - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

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

Sélection de variables structurée par régularisation jointe dans un cadre multi-tâches

(1) , (2) , (2) , (2)
1
2

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.
Fichier principal
Vignette du fichier
CAp2016_paper_13 .pdf (1.3 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01373190 , version 1 (01-06-2017)

Identifiers

  • HAL Id : hal-01373190 , version 1

Cite

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⟩
299 View
160 Download

Share

Gmail Facebook Twitter LinkedIn More