期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 59, 期 9, 页码 4183-4198出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2011.2157912
关键词
Collaborative coding; hierarchical models; sparse models; structured sparsity
资金
- NSF
- NSSEFF
- ONR
- NGA
- ARO
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1249263] Funding Source: National Science Foundation
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an l(1)-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso at the individual feature level, with the block-sparsity property of the Group Lasso, 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 advantages. We then extend this approach to the collaborative case, where a set of simultaneously coded signals share the same sparsity patternat the higher (group) level, but not necessarily at the lower (inside the group) level, obtaining the collaborative HiLasso model (C-HiLasso). Such signals then share the same active groups, or classes, but not necessarily the same active set. This model is very well suited for applications such as source identification and separation. An efficient optimization procedure, which guarantees convergence to the global optimum, is developed for these new models. The underlying presentation of the framework and optimization approach is complemented by experimental examples and theoretical results regarding recovery guarantees.
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