Hierarchical Penalization

By Marie Szafranski, Yves Grandvalet, Pierre Morizet-Mahoudeaux. 2007

37 citations recorded by the source corpus.

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Citations in the corpus

Optimization with Sparsity-Inducing Penalties (2011, background): “, [91]), and here takes specific advantage of sparsity from a computational point of view [9, 56, 69, 92, 102, 104, 113], since the subproblems that need to be solved are typically much smaller than the original one.”
Spectral k-Support Norm Regularization (2014, background): “The result holds true in the more general case that ⇥ is a bounded convex subset of the strictly positive orthant (for related results see [14, 15, 16, 17, 18, 19] and references therein).”
Sparse regression using mixed norms (2009, methodology): “Another example is provided by the hierarchical penalization [29] introduced by Szafranski and Grandvalet, leading to a mixed norm (cid:96) 43 , 1 , which is obtained after a hierarchical modeling of the variables.”
Adaptive Multi-Task Lasso: with Application to eQTL Detection (2010, background): “The hierarchical penalization [6] can achieve a smooth shrinkage effect for variables within a group, but it cannot achieve within-group sparsity.”
Structured Sparsity: from Mixed Norms to Structured Shrinkage (2009, background): “The relationship between a hierarchical model and mixed norms is well illustrated in [9].”
Optimization for machine learning (2010, background): “This class of algorithms takes advantage of sparsity from a computational point of view (Lee et al., 2007; Szafranski et al., 2007; Bach, 2008a; Roth and Fischer, 2008; Obozinski et al., 2009; Jenatton et al., 2009; Schmidt and Murphy, 2010), since the subproblems that need to be solved are typicall”