Efficient Block and Time-Recursive Estimation of Sparse Volterra Systems
(2012) 2012 IEEE Statistical Signal Processing Workshop (SSP) p.173-176- Abstract
- We investigate the application of non-convex penalized least
squares for parameter estimation in the Volterra model. Sparsity
is promoted by introducing a weighted !q penalty on the
parameters and efficient batch and time recursive algorithms
are devised based on the cyclic coordinate descent approach.
Numerical examples illustrate the improved performance of
the proposed algorithms as compared the weighted !1 norm.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3193700
- author
- Adalbjörnsson, Stefan Ingi LU ; Glentis, George-Othan and Jakobsson, Andreas LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2012 IEEE Statistical Signal Processing Workshop (SSP), Proceedings of
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2012 IEEE Statistical Signal Processing Workshop (SSP)
- conference location
- Ann Arbor, Michigan, United States
- conference dates
- 2012-08-05 - 2012-08-08
- external identifiers
-
- wos:000309943200044
- scopus:84868247347
- ISBN
- 978-1-4673-0183-1 (online)
- DOI
- 10.1109/SSP.2012.6319651
- language
- English
- LU publication?
- yes
- id
- 83731ac1-3511-4254-b8c1-75ac2c56f902 (old id 3193700)
- date added to LUP
- 2016-04-04 10:27:11
- date last changed
- 2022-01-29 20:20:46
@inproceedings{83731ac1-3511-4254-b8c1-75ac2c56f902, abstract = {{We investigate the application of non-convex penalized least<br/><br> squares for parameter estimation in the Volterra model. Sparsity<br/><br> is promoted by introducing a weighted !q penalty on the<br/><br> parameters and efficient batch and time recursive algorithms<br/><br> are devised based on the cyclic coordinate descent approach.<br/><br> Numerical examples illustrate the improved performance of<br/><br> the proposed algorithms as compared the weighted !1 norm.}}, author = {{Adalbjörnsson, Stefan Ingi and Glentis, George-Othan and Jakobsson, Andreas}}, booktitle = {{2012 IEEE Statistical Signal Processing Workshop (SSP), Proceedings of}}, isbn = {{978-1-4673-0183-1 (online)}}, language = {{eng}}, pages = {{173--176}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Efficient Block and Time-Recursive Estimation of Sparse Volterra Systems}}, url = {{http://dx.doi.org/10.1109/SSP.2012.6319651}}, doi = {{10.1109/SSP.2012.6319651}}, year = {{2012}}, }