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Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

Hong, X., Brown, M., Chen, S. and Harris, C. J. (2004) Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality. IEE Proceedings-Control Theory and Applications, 151 (4). pp. 491-498. ISSN 1350-2379

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To link to this item DOI: 10.1049/ip-cta:20040693

Abstract/Summary

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15268
Uncontrolled Keywords:LEAST-SQUARES, EXPERIMENTAL-DESIGN, SYSTEM

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