General and Nested Wiberg Minimization: L2 and Maximum Likelihood

  • Dennis Strelow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)


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. 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 L 2 minimization and maximum likelihood estimation (MLE), presenting an L 2 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, effectively removing two of three sets of variables from a minimization. We demonstrate this idea with a nested Wiberg algorithm for L 2 projective bundle adjustment, solving for camera matrices, points, and projective depths.


Matrix Factorization Bundle Adjustment Projective Depth Simultaneous Minimization Global Motion Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dennis Strelow
    • 1
  1. 1.GoogleMountain ViewUSA

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