Application of least angle regression methods for image reconstruction in EIT

Abstract

The highly correlated predictors with each other's in linear models do not allow to determine the precisely influences of these predictors on the output variable. Directly application the least square method to estimate the unknown parameters may lead to a poor prediction. The addition of penalty depending on quantities of parameters to the least square criterion allows us to determine the biased estimators but also to reduce the variance of estimators. The Least Angle Regression was used to reconstruct the image in electrical impedance tomography.