Outliers robustness in multivariate orthogonal regression

被引：12

作者：

Calafiore, GC ^{[1
]}

机构：

[1] Politecn Torino, Dipartimento Automat & Informat, I-10129 Turin, Italy

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2000年 / 30卷 / 06期

关键词：

computer vision; influential observations; orthogonal least squares; outliers; pattern recognition; regression analysis;

D O I：

10.1109/3468.895890

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper 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.

引用

页码：674 / 679

页数：6

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