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

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MPIK-TR-170.pdf (Publisher version), 2MB
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 Creators:
Sra, S1, 2, Author              
Kim, D, Author
Schölkopf, B1, 2, Author              
Affiliations:
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: 2008-06
 Publication Status: Published in print
 Pages: 18
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Rev. Type: -
 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: -