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Online hyperparameter-free sparse estimation method
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2015 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 13, 3348-3359 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we derive an online estimator for sparse parameter vectors which, unlike the LASSO approach, does not require the tuning of any hyperparameters. The algorithm is based on a covariance matching approach and is equivalent to a weighted version of the square-root LASSO. The computational complexity of the estimator is of the same order as that of the online versions of regularized least-squares (RLS) and LASSO. We provide a numerical comparison with feasible and infeasible implementations of the LASSO and RLS to illustrate the advantage of the proposed online hyperparameter-free estimator.

Place, publisher, year, edition, pages
2015. Vol. 63, no 13, 3348-3359 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-257633DOI: 10.1109/TSP.2015.2421472ISI: 000355994200005OAI: oai:DiVA.org:uu-257633DiVA: diva2:840737
Available from: 2015-06-01 Created: 2015-07-06 Last updated: 2017-12-04Bibliographically approved

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Zachariah, DaveStoica, Peter

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