New approach for distance measurement in locally weighted regression

Abstract

This paper presents a new approach for distance measurement in locally weighted regression (LWR2) by balancing the information in both chemical and spectral spaces. The new method (LWR2) is compared with the ordinary locally weighted regression method (LWR), another modified LWR method (LWR1), and the linear calibration methods, principal component regression (PCR) and partial least squares (PLS). A simulation was conducted to study how noise in chemical and spectral spaces affects the predictive ability and stability of the LWR2 method with respect to the original LWR