The Degrees of Freedom of the Group Lasso
Language
en
Communication dans un congrès
This item was published in
International Conference on Machine Learning Workshop (ICML), 2012, Edinburgh.
English Abstract
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 ...Read more >
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.Read less <
English Keywords
sparsity
group lasso
block regularization
local variation
degrees of freedom
unbiased risk estimation
Origin
Hal imported