AI RESEARCH
Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
arXiv CS.LG
•
ArXi:2506.20533v4 Announce Type: replace-cross Robust subspace estimation is fundamental to many machine learning and data analysis tasks. Iteratively Reweighted Least Squares (IRLS) is an elegant and empirically effective approach to this problem, yet its theoretical properties remain poorly understood. This paper establishes that, under deterministic conditions, a variant of IRLS with dynamic smoothing regularization converges linearly to the underlying subspace from any initialization. We extend these guarantees to affine subspace estimation, a setting that lacks prior recovery theory.