Hyperparameter-free sparse regression of grouped variables

Ted Kronvall, Stefan Ingi Adalbjornsson, Santhosh Nadig, Andreas Jakobsson

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

In this paper, we introduce a novel framework for semi-parametric estimation of an unknown number of signals, each parametrized by a group of components. Via a reformulation of the covariance fitting criteria, we formulate a convex optimization problem over a grid of candidate representations, promoting solutions with only a few active groups. Utilizing the covariance fitting allows for a hyperparameter-free estimation procedure, highly robust against coherency between candidates, while still allowing for a computationally efficient implementation. Numerical simulations illustrate how the proposed method offers a performance similar to the group-LASSO for incoherent dictionaries, and superior performance for coherent dictionaries.

Original languageEnglish
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
PublisherIEEE Computer Society
Pages394-398
Number of pages5
ISBN (Electronic)9781538639542
DOIs
Publication statusPublished - 2017 Mar 1
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: 2016 Nov 62016 Nov 9

Conference

Conference50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/TerritoryUnited States
CityPacific Grove
Period2016/11/062016/11/09

Subject classification (UKÄ)

  • Probability Theory and Statistics
  • Signal Processing

Keywords

  • convex optimization
  • covariance fitting
  • group sparsity
  • multi-pitch estimation

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