AI

General and nested Wiberg minimization: L2 and maximum likelihood

Abstract

Wiberg matrix factorization breaks a matrix Y into low-rank factors U and V by solving for V in closed form given U, linearizing V (U) about U, and iteratively minimizing jjY UV (U)jj2 with respect to U only. This approach factors the matrix while e ectively removing V from the minimization. We generalize the Wiberg approach beyond factorization to minimize an arbitrary function that is nonlinear in each of two sets of variables. In this paper we focus on the case of L2 minimization and maximum likelihood estimation (MLE), presenting an L2 Wiberg bundle adjustment algorithm and a Wiberg MLE algorithm for Poisson matrix factorization. We also show that one Wiberg minimization can be nested inside another, e ectively removing two of three sets of variables from a minimization. We demonstrate this idea with a nested Wiberg algorithm for L2 projective bundle adjustment, solving for camera matrices, points, and projective depths.