C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework

Understand

Sparse modeling is a powerful framework for data analysis and processing.

• Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit.
• In this work we combine the sparsity-inducing property of the Lasso model at the individual feature level, with the block-sparsity property of the Group Lasso model, where sparse groups of features are jointly encoded, obtaining a sparsity pattern hierarchically structured.
• This results in the Hierarchical Lasso (HiLasso), which shows important practical modeling advantages.