Billings, S.A. and Wei, H.L. (2007) Sparse model identification using a forward orthogonal regression algorithm aided by mutual information. IEEE Transactions on Neural Networks, 18 (1). pp. 306-310. ISSN 1045-9227
Abstract
A sparse representation, with satisfactory approximation accuracy, is usually desirable in any nonlinear system identification and signal processing problem. A new forward orthogonal regression algorithm, with mutual information interference, is proposed for sparse model selection and parameter estimation. The new algorithm can be used to construct parsimonious linear-in-the-parameters models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
Keywords: | Model selection, mutual information, orthogonal least squares (OLS), parameter estimation. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Sherpa Assistant |
Date Deposited: | 20 Feb 2007 |
Last Modified: | 04 Jun 2014 11:29 |
Published Version: | http://dx.doi.org/10.1109/TNN.2006.886356 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/TNN.2006.886356 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:1970 |