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Conference Papers Year : 2012

The Degrees of Freedom of the Group Lasso


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.
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hal-00695292 , version 1 (07-05-2012)



Samuel Vaiter, Charles Deledalle, Gabriel Peyré, Jalal M. Fadili, Charles H Dossal. The Degrees of Freedom of the Group Lasso. International Conference on Machine Learning Workshop (ICML), 2012, Edinburgh, United Kingdom. ⟨hal-00695292⟩
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