Greedy Reduction Algorithms for Mixtures of Exponential Family

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

Citation Formats
T. Ardeshiri, K. Granstrom, E. Özkan, and U. Orguner, “Greedy Reduction Algorithms for Mixtures of Exponential Family,” IEEE SIGNAL PROCESSING LETTERS, vol. 22, no. 6, pp. 676–680, 2015, Accessed: 00, 2020. [Online]. Available: