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Dirac Mixture Trees for Fast Suboptimal Multi-Dimensional Density Approximation

Klumpp, Vesa; Hanebeck, Uwe D.


We consider the problem of approximating an arbitrary multi-dimensional probability density function by means of a Dirac mixture density. Instead of an optimal solution based on minimizing a global distance measure between the true density and its approximation, a fast suboptimal anytime procedure is proposed, which is based on sequentially partitioning the state space and component placement by local optimization. The proposed procedure adaptively covers the entire state space with a gradually increasing resolution. It can be efficiently implemented by means of a pre-allocated tree structure in a straightforward manner. The resulting computational complexity is linear in the number of components and linear in the number of dimensions. This allows a large number of components to be handled, which is especially useful in high-dimensional state spaces.

Volltext §
DOI: 10.5445/IR/1000034850
DOI: 10.1109/MFI.2008.4648009
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2008
Sprache Englisch
Identifikator ISBN: 978-1-4244-2143-5
KITopen-ID: 1000034850
Erschienen in Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), Seoul, Republic of Korea, August, 2008
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 593-600
Nachgewiesen in Dimensions
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