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Dynamics of the maximum marginal likelihood hyperparameter estimation in image restoration: Gradient descent versus expectation and maximization algorithm

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Title: Dynamics of the maximum marginal likelihood hyperparameter estimation in image restoration: Gradient descent versus expectation and maximization algorithm
Authors: Inoue, Jun-ichi1 Browse this author →KAKEN DB
Tanaka, Kazuyuki Browse this author
Authors(alt): 井上, 純一1
Issue Date: Jan-2002
Publisher: The American Physical Society
Journal Title: PHYSICAL REVIEW E
Volume: 65
Issue: 1
Start Page: 016125
Publisher DOI: 10.1103/PhysRevE.65.016125
PMID: 11800754
Abstract: Dynamical properties of image restoration and hyperparameter estimation are investigated by means of statistical mechanics. We introduce an exactly solvable model for image restoration and derive differential equations with respect to macroscopic quantities. From these equations, we evaluate relaxation processes of the system to the equilibrium state. Our statistical mechanical approach also enables us to investigate the hyperparameter estimation by means of maximization of the marginal likelihood by using gradient descent and the expectation and maximization algorithm from the dynamical point of view.
Rights: Copyright © 2001 American Physical Society
Relation: http://www.aps.org/
Type: article
URI: http://hdl.handle.net/2115/5509
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 井上 純一

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