PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Adaptive regularization of noisy linear inverse problems
Lars Kai Hansen, Tue Lehn-Schiøler and K. H Madsen
Eusipco 2006.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2816
Deposited By:Tue Lehn-Schiøler
Deposited On:22 November 2006