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Clustering using Sum-of-Norms Regularization: With Application to Particle Filter Output Computation
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2011 (English)In: Proceedings of the 2011 IEEE Statistical Signal Processing Workshop, 2011, 201-204 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel clustering method, formulated as a convex optimization problem. The method is based on over-parameterization and uses a sum-of-norms (SON) regularization to control the trade-off between the model fit and the number of clusters. Hence, the number of clusters can be automatically adapted to best describe the data, and need not to be specified a priori. We apply SON clustering to cluster the particles in a particle filter, an application where the number of clusters is often unknown and time varying, making SON clustering an attractive alternative.

Place, publisher, year, edition, pages
2011. 201-204 p.
Keyword [en]
Clustering, Particle filter, Sum-of-norms
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-75414DOI: 10.1109/SSP.2011.5967659ISI: 000298377500051ISBN: 978-1-4577-0569-4 (print)OAI: oai:DiVA.org:liu-75414DiVA: diva2:506576
Conference
IEEE Statistical Signal Processing Workshop, Nice, France, 28-30 June, 2011
Projects
CADICS
Funder
Swedish Research Council
Available from: 2012-02-29 Created: 2012-02-29 Last updated: 2013-09-23

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Lindsten, FredrikOhlsson, HenrikLjung, Lennart

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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf