I have sparse overdefined system of linear equations.
For example I have n variables, m equations(m>n) and k equations from m are "bad" equations that represent outliers.
Is there any methods to solve this problem?
I already found techniques such as iterative reweighred least squares,LMeds,M-estimator but I'm not sure which is my case?
Here is my task in detail:
I want to solve computer vision problem which seems to be called "panorama global registration" like described here (they use weighted least squares).
Main problem is that when I calculate pairwise relations between images (which is then become equations in my system) even if I prune weak connections by threshold I have some bogus connections, so I need robust algorithm that can handle outliers.