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Non-monotonic Poisson Likelihood Maximization
- Source :
- Technical Report of the Max Planck Institute for Biological Cybernetics
- Publication Year :
- 2008
- Publisher :
- Max Planck Institute for Biological Cybernetics, 2008.
-
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.
- Subjects :
- Physics::Medical Physics
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Technical Report of the Max Planck Institute for Biological Cybernetics
- Accession number :
- edsair.od......1874..5e01767e82255714465e7a9ba0a76048