Clustering using Sum-of-Norms Regularization: With Application to Particle Filter Output Computation
2011 (English)Report (Other academic)
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
Linköping: Linköping University Electronic Press, 2011. , 6 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2993
Clustering ; Particle filter ; Sum-of-norms
IdentifiersURN: urn:nbn:se:liu:diva-97754ISRN: LiTH-ISY-R-2993OAI: oai:DiVA.org:liu-97754DiVA: diva2:650713
FunderSwedish Research Council