Least square fitting with one parameter less
Bernd A. Berg
TL;DR
This paper presents a method to perform least squares fitting with one fewer parameter by expressing the normalization as a function of other parameters and data, simplifying the fitting process when normalization is unknown.
Contribution
It introduces a technique to eliminate the normalization parameter from least squares fitting, reducing the number of parameters by one when normalization is not known.
Findings
Reduces the number of parameters in least squares fitting by one.
Enables fitting without prior normalization knowledge.
Simplifies the fitting process in practical applications.
Abstract
It is shown that whenever the multiplicative normalization of a fitting function is not known, least square fitting by minimization can be performed with one parameter less than usual by converting the normalization parameter into a function of the remaining parameters and the data.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.