Adaptive regularization of noisy linear inverse problems |
| | Abstract | In the Bayesian modeling framework there is a close relation between regularization and the prior distribution over parameters. For prior distributions in the exponential family, we show that the optimal hyper-parameter, i.e., the optimal strength of regularization, satisfies a simple relation: The expectation
of the regularization function, i.e., takes the same value in the posterior and prior distribution. We present three examples: two simulations, and application in fMRI neuroimaging. | | Keywords | Bayes, regularization, inverse problems, fMRI | | Type | Conference paper [With referee] | | Conference | Eusipco | | Year | 2006 | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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