Combinatorial bounds on the α-divergence of univariate mixture models
Online Publication Date2017-06-20
Print Publication Date2017-03
Permanent link to this recordhttp://hdl.handle.net/10754/625623
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AbstractWe derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.
CitationNielsen F, Sun K (2017) Combinatorial bounds on the α-divergence of univariate mixture models. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/ICASSP.2017.7953003.
Conference/Event name2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017