Online hyperparameter-free sparse estimation method
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
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.
IdentifiersURN: urn:nbn:se:uu:diva-257633DOI: 10.1109/TSP.2015.2421472ISI: 000355994200005OAI: oai:DiVA.org:uu-257633DiVA: diva2:840737