Title :
Outliers robustness in multivariate orthogonal regression
Author :
Calafiore, Giuseppe Carlo
Author_Institution :
Dipartimento di Autom. e Inf., Politecnico di Torino, Italy
fDate :
11/1/2000 12:00:00 AM
Abstract :
Deals with the problem of multivariate affine regression in the presence of outliers in the data. The method discussed is based on weighted orthogonal least squares. The weights associated with the data satisfy a suitable optimality criterion and are computed by a two-step algorithm requiring a RANSAC step and a gradient-based optimization step. Issues related to the breakdown point of the method are discussed, and examples of application on various real multidimensional data sets are reported in the paper
Keywords :
estimation theory; least squares approximations; matrix algebra; statistical analysis; RANSAC step; breakdown point; gradient-based optimization step; multidimensional data sets; multivariate affine regression; multivariate orthogonal regression; optimality criterion; outliers robustness; two-step algorithm; weighted orthogonal least squares; Electric breakdown; Equations; Gaussian processes; Helium; History; Least squares methods; Multidimensional systems; Noise robustness; Pattern recognition; Regression analysis;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/3468.895890
