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Objective Selection of Hyperparameter for EIT B M Graham, A Adler

Summary: Objective Selection of Hyperparameter for EIT
B M Graham, A Adler
School of Information Technology and Engineering (SITE), University of
Ottawa, Canada
E-mail: graham.bm@sympatico.ca, adler@site.uottawa.ca
Abstract. An algorithm for objectively calculating the hyperparameter for the
class of linearized one step 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 balance 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 evaluate an compare and evaluate 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


Source: Adler, Andy - Department of Systems and Computer Engineering, Carleton University


Collections: Computer Technologies and Information Sciences