General and Nested Wiberg Minimization
Venue
Computer Vision and Pattern Recognition, IEEE (2012)
Publication Year
2012
Authors
Dennis Strelow
BibTeX
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 ||Y - UV(U)||_2 with respect to U only. This approach factors the matrix
while effectively removing V from the minimization. Recently Eriksson and van den
Hengel extended this approach to L1, minimizing ||Y - UV(U)||_1. We generalize
their approach beyond factorization to minimize an arbitrary function that is
nonlinear in each of two sets of variables. We demonstrate the idea with a
practical Wiberg algorithm for L1 bundle adjustment. We also show that one Wiberg
minimization can be nested inside another, effectively removing two of three sets
of variables from a minimization. We demonstrate this idea with a nested Wiberg
algorithm for L1 projective bundle adjustment, solving for camera matrices, points,
and projective depths. We also revisit L1 factorization, giving a greatly
simplified presentation of Wiberg L1 factorization, and presenting a successive
linear programming factorization algorithm. Successive linear programming
outperforms L1 Wiberg for most large inputs, establishing a new state-of-the-art
for for those cases.
