Generalized sparse covariance-based estimation
Swärd, Johan LU ; Adalbjörnsson, Stefan I. LU and Jakobsson, Andreas LU
(2018)
In Signal Processing
143.
p.311-319
- Abstract
In this work, we generalize the recent sparse iterative covariance-based estimator (SPICE) by extending the problem formulation to allow for different norm constraints on the signal and noise parameters in the covariance model. The resulting extended SPICE algorithm offers the same benefits as the regular SPICE algorithm, including being hyper-parameter free, but the choice of norms allows further control of the sparsity in the resulting solution. We also show that the proposed extension is equivalent to solving a penalized regression problem, providing further insight into the differences between the extended and original SPICE formulations. The performance of the method is evaluated for different choices of norms, indicating the... (More)
In this work, we generalize the recent sparse iterative covariance-based estimator (SPICE) by extending the problem formulation to allow for different norm constraints on the signal and noise parameters in the covariance model. The resulting extended SPICE algorithm offers the same benefits as the regular SPICE algorithm, including being hyper-parameter free, but the choice of norms allows further control of the sparsity in the resulting solution. We also show that the proposed extension is equivalent to solving a penalized regression problem, providing further insight into the differences between the extended and original SPICE formulations. The performance of the method is evaluated for different choices of norms, indicating the preferable performance of the extended formulation as compared to the original SPICE algorithm. Finally, we introduce two implementations of the proposed algorithm, one gridless formulating for the sinusoidal case, resulting in a semi-definite programming problem, and one grid-based, for which an efficient implementation is given.
(Less)
- author
- Swärd, Johan
LU
; Adalbjörnsson, Stefan I.
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2018-02-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Convex optimization, Covariance fitting, Sparse reconstruction
- in
- Signal Processing
- volume
- 143
- pages
- 9 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85029722145
- ISSN
- 0165-1684
- DOI
- 10.1016/j.sigpro.2017.09.010
- project
- eSSENCE@LU 4:2 - Efficient data acquisition and analyses for modern multidimensional spectroscopy"
- language
- English
- LU publication?
- yes
- id
- 85f875c6-4594-44b9-98fa-8eee17fd8d22
- date added to LUP
- 2017-10-05 10:50:41
- date last changed
- 2022-04-17 08:08:47
@article{85f875c6-4594-44b9-98fa-8eee17fd8d22,
abstract = {{<p>In this work, we generalize the recent sparse iterative covariance-based estimator (SPICE) by extending the problem formulation to allow for different norm constraints on the signal and noise parameters in the covariance model. The resulting extended SPICE algorithm offers the same benefits as the regular SPICE algorithm, including being hyper-parameter free, but the choice of norms allows further control of the sparsity in the resulting solution. We also show that the proposed extension is equivalent to solving a penalized regression problem, providing further insight into the differences between the extended and original SPICE formulations. The performance of the method is evaluated for different choices of norms, indicating the preferable performance of the extended formulation as compared to the original SPICE algorithm. Finally, we introduce two implementations of the proposed algorithm, one gridless formulating for the sinusoidal case, resulting in a semi-definite programming problem, and one grid-based, for which an efficient implementation is given.</p>}},
author = {{Swärd, Johan and Adalbjörnsson, Stefan I. and Jakobsson, Andreas}},
issn = {{0165-1684}},
keywords = {{Convex optimization; Covariance fitting; Sparse reconstruction}},
language = {{eng}},
month = {{02}},
pages = {{311--319}},
publisher = {{Elsevier}},
series = {{Signal Processing}},
title = {{Generalized sparse covariance-based estimation}},
url = {{http://dx.doi.org/10.1016/j.sigpro.2017.09.010}},
doi = {{10.1016/j.sigpro.2017.09.010}},
volume = {{143}},
year = {{2018}},
}