Non-monotonic Poisson Likelihood Maximization

Sra, S; Kim, D; Schölkopf, B

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Non-monotonic Poisson Likelihood Maximization

Sra, S., Kim, D., & Schölkopf, B.(2008). Non-monotonic Poisson Likelihood Maximization (170). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C90D-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8713-0

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Sra, S^{1, 2}, Author
Kim, D, Author
Schölkopf, B^{1, 2}, Author

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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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Abstract: This report summarizes the theory and some main applications of a new non-monotonic algorithm for
maximizing a Poisson Likelihood, which for Positron Emission Tomography (PET) is equivalent to minimizing
the associated Kullback-Leibler Divergence, and for Transmission Tomography is similar to maximizing the dual
of a maximum entropy problem. We call our method non-monotonic maximum likelihood (NMML) and show
its application to different problems such as tomography and image restoration. We discuss some theoretical
properties such as convergence for our algorithm. Our experimental results indicate that speedups obtained via our
non-monotonic methods are substantial.

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Dates: Published in Print: 2008-06

Publication Status: Published in print

Pages: 18

Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics

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Identifiers: Report Nr.: 170
BibTex Citekey: 5831

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Title: Technical Report of the Max Planck Institute for Biological Cybernetics

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Pages: - Volume / Issue: 170 Sequence Number: - Start / End Page: - Identifier: -