Sparsity-aware estimation of nonlinear Volterra kernels

Vassilis Kekatos, Daniele Angelosante, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

The Volterra series expansion has well-documented merits for modeling smooth nonlinear systems. Given that nature itself is parsimonious and models with minimal degrees of freedom are attractive from a system identification viewpoint, estimating sparse Volterra models is of paramount importance. Based on input-output data, existing estimators of Volterra kernels are sparsity agnostic because they rely on standard (possibly recursive) least-squares approaches. Instead, the present contribution develops batch and recursive algorithms for estimating sparse Volterra kernels using the least-absolute shrinkage and selection operator (Lasso) along with its recent weighted and online variants. Analysis and simulations demonstrate that weighted (recursive) Lasso has the potential to obviate the "curse of dimensionality," especially in the under-determined case where input-output data are less than the number of unknowns dictated by the order of the expansion and the memory of the kernels.

Original languageEnglish (US)
Title of host publicationCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages129-132
Number of pages4
DOIs
StatePublished - Dec 1 2009
Event2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009 - Aruba, Netherlands
Duration: Dec 13 2009Dec 16 2009

Publication series

NameCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Other

Other2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009
CountryNetherlands
CityAruba
Period12/13/0912/16/09

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    Kekatos, V., Angelosante, D., & Giannakis, G. B. (2009). Sparsity-aware estimation of nonlinear Volterra kernels. In CAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (pp. 129-132). [5413323] (CAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing). https://doi.org/10.1109/CAMSAP.2009.5413323