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Linear regression with a sparse parameter vector
KTH, School of Electrical Engineering (EES).
2006 (English)In: 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol III, Proceedings: signal processing theory and methods, design and implementation of signal processing systems, industry technology tracks, 2006, 309- p.Conference paper, Published paper (Refereed)
Abstract [en]

We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive a computationally efficient approximation to the minimum mean-square error (MMSE) estimate of the parameter vector. The performance of the so-obtained estimate is illustrated via numerical examples.

Place, publisher, year, edition, pages
2006. 309- p.
Series
International Conference on Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149
National Category
Fluid Mechanics and Acoustics
Identifiers
URN: urn:nbn:se:kth:diva-42409DOI: 10.1109/ICASSP.2006.1660652ISI: 000245559903101Scopus ID: 2-s2.0-33947647329OAI: oai:DiVA.org:kth-42409DiVA: diva2:447259
Conference
31st IEEE International Conference on Acoustics, Speech and Signal Processing. Toulouse, FRANCE. MAY 14-19, 2006
Note
QC 20111011Available from: 2011-10-11 Created: 2011-10-10 Last updated: 2011-10-11Bibliographically approved

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Larsson, Erik G.
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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