A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances. (Q1410866)
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scientific article; zbMATH DE number 1993216
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances. |
scientific article; zbMATH DE number 1993216 |
Statements
A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances. (English)
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15 October 2003
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Given a family of curves or surfaces an important problem is that of finding a member of the family which gives a best fit to \(m\) given data points. The criterion used is orthogonal distance regression where the sum of squares of orthogonal distances from the data points to the surface is minimized. The paper proposes a unified framework for recent methods for this problem and makes some comparisons.
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curve fitting
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surface fitting
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least squares method
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Gauss-Newton method
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orthogonal distances regression
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comparison of methods
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