Distance regression by Gauss-Newton-type methods and iteratively re-weighted least-squares
type: Item
instance of: scholarly article
title: Distance regression by Gauss-Newton-type methods and iteratively re-weighted least-squares
zbMATH Open document ID: 1183.65014
author: Martin Aigner
author: Bert Jüttler
published in: Computing
publication date: 2009-11-06T00:00:00Z
review text: A generalized problem of fitting a curve or surface to given measurement data is presented. The solution is focused on the situations when the usual least-squares approach is not suitable. The generalization of the fitting problem lies in minimizing the sum of the so called norm-like functions applied to the residual vectors that connect the measured points with associated points on the fitted curve or surface. This approach represents an extension of the iteratively re-weighted least-squares method (Gauss-Newton-type method) for minimizing a sum of norm-like functions of scalar residuals. The obtained results can be used in various applications of computational geometry -- reconstruction of geometric models from point cloud data, regression analysis, image segmentation, pattern recognition, etc.
reviewed by: Ivana Linkeová
Mathematics Subject Classification ID: 65D10
Mathematics Subject Classification ID: 65F20
Mathematics Subject Classification ID: 65D18
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 5627068
zbMATH Keywords: curve fitting
zbMATH Keywords: surface fitting
zbMATH Keywords: least-squares
zbMATH Keywords: iteratively re-weighted least squares
zbMATH Keywords: Gauss-Newton method
zbMATH Keywords: computational geometry
zbMATH Keywords: regression analysis
zbMATH Keywords: image segmentation
zbMATH Keywords: pattern recognition
describes a project that uses: KELLEY
MaRDI profile type: Publication
full work available at URL: s00607-009-0055-6
OpenAlex ID: W2011988791
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DOI: 10.1007/S00607-009-0055-6
description: scientific article; zbMATH DE number 5627068