A Bayesian framework for regularization

Daniel Keren, Michael Werman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Regularization looks for an interpolating function which is close to the data and also "smooth". This function is obtained by minimizing an error functional which is the weighted sum of a "fidelity term"and a "smoothness term". However, using only one set of weights does not guarantee that this function will be the MAP estimate. One has to consider all possible weights in order to find the MAP function. Also, using only one combination of weights makes the algorithm very sensitive to the data. The solution suggested here is through the Bayesian approach: A probability distribution over all weights is constructed and all weights are considered when reconstructing the function or computing the expectation of a linear functional on the function space.

Original languageAmerican English
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages72-76
Number of pages5
ISBN (Electronic)0818662751
DOIs
StatePublished - 1994
Event12th IAPR International Conference on Pattern Recognition - Conference C: Signal Processing - Conference D: Parallel Computing, ICPR 1994 - Jerusalem, Israel
Duration: 9 Oct 199413 Oct 1994

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Conference

Conference12th IAPR International Conference on Pattern Recognition - Conference C: Signal Processing - Conference D: Parallel Computing, ICPR 1994
Country/TerritoryIsrael
CityJerusalem
Period9/10/9413/10/94

Bibliographical note

Publisher Copyright:
© 1994 IEEE.

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