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Hyperparameter-free sparse regression of grouped variables

Kronvall, Ted LU ; Adalbjornsson, Stefan Ingi LU ; Nadig, Santhosh and Jakobsson, Andreas LU (2017) 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 In Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 p.394-398
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.

Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
convex optimization, covariance fitting, group sparsity, multi-pitch estimation
in
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
pages
5 pages
publisher
IEEE Computer Society
conference name
50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
external identifiers
  • scopus:85016250766
ISBN
9781538639542
DOI
10.1109/ACSSC.2016.7869067
language
English
LU publication?
yes
id
d1c97ff7-ea12-4766-84fb-72369c241aed
date added to LUP
2017-04-12 15:01:22
date last changed
2018-01-07 11:59:14
@inproceedings{d1c97ff7-ea12-4766-84fb-72369c241aed,
  abstract     = {<p>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.</p>},
  author       = {Kronvall, Ted and Adalbjornsson, Stefan Ingi and Nadig, Santhosh and Jakobsson, Andreas},
  booktitle    = {Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016},
  isbn         = {9781538639542},
  keyword      = {convex optimization,covariance fitting,group sparsity,multi-pitch estimation},
  language     = {eng},
  month        = {03},
  pages        = {394--398},
  publisher    = {IEEE Computer Society},
  title        = {Hyperparameter-free sparse regression of grouped variables},
  url          = {http://dx.doi.org/10.1109/ACSSC.2016.7869067},
  year         = {2017},
}