Greedy Reduction Algorithms for Mixtures of Exponential Family

Date

2015-06-01

Author

Ardeshiri, Tohid
Granstrom, Karl
Özkan, Emre
Orguner, Umut

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In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.

Subject Keywords

Exponential Family, Extended Target, Integral Square Error, Kullback-Leibler Divergence, Mixture Density, Mixture Reduction, Target Tracking

URI

https://hdl.handle.net/11511/42511

Journal

IEEE SIGNAL PROCESSING LETTERS

DOI

https://doi.org/10.1109/lsp.2014.2367154

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

T. Ardeshiri, K. Granstrom, E. Özkan, and U. Orguner, “Greedy Reduction Algorithms for Mixtures of Exponential Family,” IEEE SIGNAL PROCESSING LETTERS, pp. 676–680, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42511.