A generalization of the sparse iterative covariance-based estimator
Sward, Johan LU ; Adalbjornsson, Stefan Ingi LU and Jakobsson, Andreas LU
(2017)
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
p.3954-3958
- Abstract
In this work, we extend the popular sparse iterative covariance-based estimator (SPICE) by generalizing the formulation to allow for different norm constraint on the signal and noise parameters in the covariance model. For any choice of norms, the resulting generalized SPICE method enjoys the same benefits as the regular SPICE method, including being hyperparameter free, although the choice of norm is shown to govern the sparsity in the resulting solution. Furthermore, we show that there is a connection between the generalized SPICE and a penalized regression problem, both for the case were one allows the noise parameters to differ for each sample, and when treating each noise parameter as being equal. We examine the performance of the... (More)
In this work, we extend the popular sparse iterative covariance-based estimator (SPICE) by generalizing the formulation to allow for different norm constraint on the signal and noise parameters in the covariance model. For any choice of norms, the resulting generalized SPICE method enjoys the same benefits as the regular SPICE method, including being hyperparameter free, although the choice of norm is shown to govern the sparsity in the resulting solution. Furthermore, we show that there is a connection between the generalized SPICE and a penalized regression problem, both for the case were one allows the noise parameters to differ for each sample, and when treating each noise parameter as being equal. We examine the performance of the method for different choices of norms, and compare the results to the original SPICE method, showing the benefits of using the generalized version. We also provide a way of solving the generalized SPICE using a gridless method, which solves a semi-definite programming problem.
(Less)
- author
- Sward, Johan
LU
; Adalbjornsson, Stefan Ingi
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2017-06-16
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- convex optimization, Covariance fitting, sparse reconstruction
- host publication
- 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
- article number
- 7952898
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
- conference location
- New Orleans, United States
- conference dates
- 2017-03-05 - 2017-03-09
- external identifiers
-
- scopus:85023745907
- ISBN
- 9781509041176
- DOI
- 10.1109/ICASSP.2017.7952898
- language
- English
- LU publication?
- yes
- id
- f2882b4b-f828-4814-8fa0-3f309ca6a9bd
- date added to LUP
- 2017-07-27 13:38:48
- date last changed
- 2025-04-04 14:46:23
@inproceedings{f2882b4b-f828-4814-8fa0-3f309ca6a9bd,
abstract = {{<p>In this work, we extend the popular sparse iterative covariance-based estimator (SPICE) by generalizing the formulation to allow for different norm constraint on the signal and noise parameters in the covariance model. For any choice of norms, the resulting generalized SPICE method enjoys the same benefits as the regular SPICE method, including being hyperparameter free, although the choice of norm is shown to govern the sparsity in the resulting solution. Furthermore, we show that there is a connection between the generalized SPICE and a penalized regression problem, both for the case were one allows the noise parameters to differ for each sample, and when treating each noise parameter as being equal. We examine the performance of the method for different choices of norms, and compare the results to the original SPICE method, showing the benefits of using the generalized version. We also provide a way of solving the generalized SPICE using a gridless method, which solves a semi-definite programming problem.</p>}},
author = {{Sward, Johan and Adalbjornsson, Stefan Ingi and Jakobsson, Andreas}},
booktitle = {{2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings}},
isbn = {{9781509041176}},
keywords = {{convex optimization; Covariance fitting; sparse reconstruction}},
language = {{eng}},
month = {{06}},
pages = {{3954--3958}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{A generalization of the sparse iterative covariance-based estimator}},
url = {{http://dx.doi.org/10.1109/ICASSP.2017.7952898}},
doi = {{10.1109/ICASSP.2017.7952898}},
year = {{2017}},
}