The Degrees of Freedom of the Group Lasso
Langue
en
Communication dans un congrès
Ce document a été publié dans
International Conference on Machine Learning Workshop (ICML), 2012, Edinburgh.
Résumé en anglais
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overdetermined linear regression model. Such a regularization is known to promote sparsity patterns structured as nonoverlapping ...Lire la suite >
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overdetermined linear regression model. Such a regularization is known to promote sparsity patterns structured as nonoverlapping groups of coefficients. Our main contribution provides a local parameterization of the solution with respect to the observations. As a byproduct, we give an unbiased estimate of the degrees of freedom of the group Lasso. Among other applications of such results, one can choose in a principled and objective way the regularization parameter of the Lasso through model selection criteria.< Réduire
Mots clés en anglais
sparsity
group lasso
block regularization
local variation
degrees of freedom
unbiased risk estimation
Origine
Importé de halUnités de recherche