I am performing orthogonal distance regression on a set of points to find the best fit plane. I am using the method described on this page http://www.infogoaround.org/JBook/LSQ_Plane.html
The problem is that I end up with a linear homogenous system of the form:
Ax = 0
And I don't know a good computational way to solve this. (The author of the webpage says it is just an eigenvalue problem, but that looks nothing like an eigenvalue problem to me.)
A is a symmetric 3x3 matrix. I am writing some code to solve this (custom system so I can't use matrix libraries) and I'm not sure the best way to do it.
I would think there is a better/easier/more efficient method than Gauss-Jordan, but I don't know what that would be.
SVD seems to be promising, but the Wikipedia page makes it seem too intimidating to program!