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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 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
host publication
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
conference location
Pacific Grove, United States
conference dates
2016-11-06 - 2016-11-09
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
2019-03-08 02:33:41
@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},
  isbn         = {9781538639542},
  keyword      = {convex optimization,covariance fitting,group sparsity,multi-pitch estimation},
  language     = {eng},
  location     = {Pacific Grove, United States},
  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},
}