Clustering using Sum-of-Norms Regularization: With Application to Particle Filter Output Computation
2011 (English)In: Proceedings of the 2011 IEEE Statistical Signal Processing Workshop, 2011, 201-204 p.Conference paper (Refereed)
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.
Clustering, Particle filter, Sum-of-norms
IdentifiersURN: urn:nbn:se:liu:diva-75414DOI: 10.1109/SSP.2011.5967659ISI: 000298377500051ISBN: 978-1-4577-0569-4OAI: oai:DiVA.org:liu-75414DiVA: diva2:506576
IEEE Statistical Signal Processing Workshop, Nice, France, 28-30 June, 2011
FunderSwedish Research Council