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Inference and mixture modeling with the Elliptical Gamma Distribution

MPG-Autoren
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Hosseini,  R
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Sra,  S
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Theis,  L
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bethge,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Hosseini, R., Sra, S., Theis, L., & Bethge, M. (2016). Inference and mixture modeling with the Elliptical Gamma Distribution. Computational Statistics Data Analysis, 101, 29-43. doi:10.1016/j.csda.2016.02.009.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-797C-E
Zusammenfassung
The authors study modeling and inference with the Elliptical Gamma Distribution (EGD). In particular, Maximum likelihood (ML) estimation for EGD scatter matrices is considered, a task for which the authors present new fixed-point algorithms. The algorithms are shown to be efficient and convergent to global optima despite non-convexity. Moreover, they turn out to be much faster than both a well-known iterative algorithm of Kent Tyler and sophisticated manifold optimization algorithms. Subsequently, the ML algorithms are invoked as subroutines for estimating parameters of a mixture of EGDs. The performance of the methods is illustrated on the task of modeling natural image statistics—the proposed EGD mixture model yields the most parsimonious model among several competing approaches.