An INFO-GAP approach to linear regression

M. Zachsenhouse, S. Nemets, A. Yoffe, Y. Ben-Haim, Mikhail A. Lebedev, Miguel A.L. Nicolelis

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

1 Citation (Scopus)

Abstract

Linear regression with high uncertainties in the measurements, model structure and model permanence is a major challenging problem. Standard regression techniques are based on optimizing a certain performance criterion, usually the mean squared error, and are highly sensitive to uncertainties. Regularization methods have been developed to address the problem of measurement uncertainty, but choosing the regularization parameter under severe uncertainties is problematic. Here we develop an alternative regression methodology based on satisficing rather than optimizing the performance criterion while maximizing the robustness to uncertainties. Uncertainties are represented by info-gap models which entail an unbounded family of nested sets of measurements parameterized by a non-probabilistic horizon of uncertainty. We prove and demonstrate that the robust-satisficing solution is different from the optimal least squares solution and that the infogap approach can provide higher robustness to uncertainty.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesIII800-III803
Publication statusPublished - 2006
Externally publishedYes
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 14 May 200619 May 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Country/TerritoryFrance
CityToulouse
Period14/05/0619/05/06

Fingerprint

Dive into the research topics of 'An INFO-GAP approach to linear regression'. Together they form a unique fingerprint.

Cite this