Naslov
Composite distance based approach to von Mises mixture reduction

Autori
Bukal, Mario ; Marković, Ivan ; Petrović, Ivan

Izvornik
Information fusion (1566-2535) 20 (2014); 136-145

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
von Mises mixture; mixture component number reduction; composite distance measure; generalized k-means algorithm; trajectory shape analysis

Sažetak
This paper presents a systematic approach for component number reduction in mixtures of exponential families, putting a special emphasis on the von Mises mixtures. We propose to formulate the problem as an optimization problem utilizing a new class of computationally tractable composite distance measures as cost functions, namely the composite Rényi alpha-divergences, which include the composite Kullback-Leibler distance as a special case. Furthermore, we prove that the composite divergence bounds from above the corresponding intractable Rényi alpha-divergence between a pair of mixtures. As a solution to the optimization problem we synthesize that two existing suboptimal solution strategies, the generalized $k$-means and a pairwise merging approach, are actually minimization methods for the composite distance measures. Moreover, in the present paper the existing joining algorithm is also extended for comparison purposes.The algorithms are implemented and their reduction results are compared and discussed on two examples of von Mises mixtures: a synthetic mixture and a real-world mixture used in people trajectory shape analysis.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti

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