Group-Sparse Model Selection: Hardness and Relaxations

13 Mar 2013Luca BaldassarreNirav BhanVolkan CevherAnastasios KyrillidisSiddhartha Satpathi

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these models is the recovery of "interpretable" signals through the identification of their constituent groups... (read more)

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