DocumentCode :
2503878
Title :
Clustering using sum-of-norms regularization: With application to particle filter output computation
Author :
Lindsten, Fredrik ; Ohlsson, Henrik ; Ljung, Lennart
Author_Institution :
Div. of Autom. Control, Linkoping Univ., Linköping, Sweden
fYear :
2011
fDate :
28-30 June 2011
Firstpage :
201
Lastpage :
204
Abstract :
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 tradeoff 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.
Keywords :
convex programming; particle filtering (numerical methods); pattern clustering; clustering; convex optimization problem; over-parameterization; particle filter output computation; sum-of-norms regularization; time varying; Clustering algorithms; Clustering methods; Kernel; Optimization; Roads; Target tracking; Vehicles; Clustering; particle filter; sum-of-norms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2011 IEEE
Conference_Location :
Nice
ISSN :
pending
Print_ISBN :
978-1-4577-0569-4
Type :
conf
DOI :
10.1109/SSP.2011.5967659
Filename :
5967659
Link To Document :
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