Learning the Structure for Structured Sparsity - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Signal Processing Year : 2015

Learning the Structure for Structured Sparsity


Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity rely on prior knowledge on how to weight (or how to penalize) individual subsets of variables during the subset selection process, which is not available in general. Inferring group weights from data is a key open research problem in structured sparsity. In this paper, we propose a Bayesian approach to the problem of group weight learning. We model the group weights as hyperparameters of heavy-tailed priors on groups of variables and derive an approximate inference scheme to infer these hyperparameters. We empirically show that we are able to recover the model hyperparameters when the data are generated from the model, and we demonstrate the utility of learning weights in synthetic and real denoising problems.
Fichier principal
Vignette du fichier
paper.pdf (2.41 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00986380 , version 1 (02-05-2014)
hal-00986380 , version 2 (17-10-2014)
hal-00986380 , version 3 (10-03-2015)
hal-00986380 , version 4 (15-09-2015)



Nino Shervashidze, Francis Bach. Learning the Structure for Structured Sparsity. IEEE Transactions on Signal Processing, 2015, 63 (18), pp.4894 - 4902. ⟨10.1109/TSP.2015.2446432⟩. ⟨hal-00986380v4⟩
798 View
1064 Download



Gmail Facebook Twitter LinkedIn More