, 86:73 | Cite as

Distance regression by Gauss–Newton-type methods and iteratively re-weighted least-squares

  • Martin Aigner
  • Bert Jüttler


We discuss the problem of fitting a curve or surface to given measurement data. In many situations, the usual least-squares approach (minimization of the sum of squared norms of residual vectors) is not suitable, as it implicitly assumes a Gaussian distribution of the measurement errors. In those cases, it is more appropriate to minimize other functions (which we will call norm-like functions) of the residual vectors. This is well understood in the case of scalar residuals, where the technique of iteratively re-weighted least-squares, which originated in statistics (Huber in Robust statistics, 1981) is known to be a Gauss–Newton-type method for minimizing a sum of norm-like functions of the residuals. We extend this result to the case of vector-valued residuals. It is shown that simply treating the norms of the vector-valued residuals as scalar ones does not work. In order to illustrate the difference we provide a geometric interpretation of the iterative minimization procedures as evolution processes.


Curve and surface fitting Iteratively re-weighted least squares Gauss–Newton method Fitting by evolution 

Mathematics Subject Classification (2000)

65D17 68U07 


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Institute of Applied GeometryJohannes Kepler UniversityLinzAustria

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