Sparse model identification using a forward orthogonal regression algorithm aided by mutual information

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

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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.

Item Type:	Article
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.
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:	08 Feb 2013 16:51
Published Version:	http://dx.doi.org/10.1109/TNN.2006.886356
Status:	Published
Publisher:	IEEE
Refereed:	Yes
Identification Number:	10.1109/TNN.2006.886356
URI:	http://eprints.whiterose.ac.uk/id/eprint/1970

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