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Connections Between Optimisation-Based Regressor Selection and Analysis of Variance
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2006 (English)In: Proceedings of the 45th IEEE Conference on Decision and Control, San Diego: IEEE CSS , 2006, 4907-4914 p.Conference paper, Published paper (Refereed)
Abstract [en]

Earlier contributions have shown that Analysis of Variance (ANOVA) can be successfully used for finding good regressors for nonlinear models in a nonlinear black-box system identification context. In this paper, it is shown that the ANOVA problem can be recast as an optimisation problem. Two modified, convex versions of the ANOVA optimisation problem are then proposed, and it turns out that they are closely related to the nn-garrote and wavelet shrinkage methods, respectively. In the case of balanced data, it is also shown that the methods have a nice orthogonality property in the sense that different groups of parameters can be computed independently.

Place, publisher, year, edition, pages
San Diego: IEEE CSS , 2006. 4907-4914 p.
Keyword [en]
ANOVA, 1-norm, Lasso, Nn-garrote, Nonlinear identification, Regressor selection
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-36932DOI: 10.1109/CDC.2006.377519Local ID: 33079ISBN: 1-4244-0171-2 (print)OAI: oai:DiVA.org:liu-36932DiVA: diva2:257781
Conference
45th IEEE Conference on Decision and Control, San Diego, CA, USA, December, 2006
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-02-26

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Roll, JacobLind, IngelaLjung, Lennart

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