Conference papers
Selection of variables structured by regularization in a multi-task framework
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.
Cited literature [15 references]
https://hal-amu.archives-ouvertes.fr/hal-01373190
Contributor : Valentin Emiya <>
Submitted on : Thursday, June 1, 2017 - 9:14:29 AM
Last modification on : Wednesday, December 9, 2020 - 3:13:26 AM
Long-term archiving on: : Wednesday, September 6, 2017 - 6:12:04 PM
Contributor : Valentin Emiya <>
Submitted on : Thursday, June 1, 2017 - 9:14:29 AM
Last modification on : Wednesday, December 9, 2020 - 3:13:26 AM
Long-term archiving on: : Wednesday, September 6, 2017 - 6:12:04 PM
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CAp2016_paper_13 .pdf
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