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An elastic net orthogonal forward regression algorithm

An elastic net orthogonal forward regression algorithm
An elastic net orthogonal forward regression algorithm
In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Hong, Xia and Chen, Sheng (2012) An elastic net orthogonal forward regression algorithm. 16th IFAC Symposium on System Identification, Belgium. 11 - 13 Jul 2012. 6 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

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Published date: July 2012
Venue - Dates: 16th IFAC Symposium on System Identification, Belgium, 2012-07-11 - 2012-07-13
Organisations: Southampton Wireless Group

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Local EPrints ID: 340757
URI: http://eprints.soton.ac.uk/id/eprint/340757
PURE UUID: ae2acfbb-9bfb-4431-9649-cb43a9f57632

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Date deposited: 05 Jul 2012 09:07
Last modified: 19 Jul 2019 21:56

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