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Connection between SPICE and Square-Root LASSO for sparse parameter estimation
Department of Electronic and Computer Engineering, HKUST, Hong Kong.
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.
2014 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 95, 10-14 p.Article in journal (Refereed) Published
Abstract [en]

In this note we show that the sparse estimation technique named Square-Root LASSO (SR-LASSO) is connected to a previously introduced method named SPICE. More concretely we prove that the SR-LASSO with a unit weighting factor is identical to SPICE. Furthermore we show via numerical simulations that the performance of the SR-LASSO changes insignificantly when the weighting factor is varied. SPICE stands for sparse iterative covariance-based estimation and LASSO for least absolute shrinkage and selection operator.

Place, publisher, year, edition, pages
2014. Vol. 95, 10-14 p.
Keyword [en]
LASSO, Square-Root LASSO, SPICE, Covariance fitting, Sparse parameter estimation
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-209143DOI: 10.1016/j.sigpro.2013.08.011ISI: 000326912000002OAI: oai:DiVA.org:uu-209143DiVA: diva2:656119
Funder
Swedish Research CouncilEU, European Research Council
Available from: 2013-10-15 Created: 2013-10-15 Last updated: 2017-12-06Bibliographically approved

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Babu, PrabhuStoica, Peter

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