Linked Life Data

16636421

Source:http://linkedlifedata.com/resource/pubmed/id/16636421

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2006-4-25
pubmed:abstractText
An algorithm for objectively calculating the hyperparameter for linearized one-step electrical impedance tomography (EIT) image reconstruction algorithms is proposed and compared to existing strategies. EIT is an ill-conditioned problem in which regularization is used to calculate a stable and accurate solution by incorporating some form of prior knowledge into the solution. A hyperparameter is used to control the trade-off between conformance to data and conformance to the prior. A remaining challenge is to develop and validate methods of objectively selecting the hyperparameter. In this paper, we evaluate and compare five different strategies for hyperparameter selection. We propose a calibration-based method of objective hyperparameter selection, called BestRes, that leads to repeatable and stable image reconstructions that are indistinguishable from heuristic selections. Results indicate: (1) heuristic selections of hyperparameter are inconsistent among experts, (2) generalized cross-validation approaches produce under-regularized solutions, (3) L-curve approaches are unreliable for EIT and (4) BestRes produces good solutions comparable to expert selections. Additionally, we show that it is possible to reliably detect an inverse crime based on analysis of these parameters.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0967-3334
pubmed:author
pubmed:issnType
Print
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
S65-79
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Objective selection of hyperparameter for EIT.
pubmed:affiliation
School of Information Technology and Engineering, University of Ottawa, Canada. graham.bm@sympatico.ca
pubmed:publicationType
Journal Article, Evaluation Studies