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Zhao, Wanqing 
ORCID: https://orcid.org/0000-0001-6160-9547, Beach, Thomas H. and Rezgui, Yacine
(2017)
Efficient least angle regression for identification of linear-in-the-parameters models.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473 (2198).
ISSN 1364-5021
Abstract
Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 03 Jul 2020 23:58 |
| Last Modified: | 18 Aug 2023 00:43 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/75906 |
| DOI: | 10.1098/rspa.2016.0775 |
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