Fast Approximate L_infty Minimization: Speeding Up Robust Regression
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Minimization of the L_∞ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression. However, current techniques for solving the problem at the heart of L_∞ norm minimization are slow, and therefore cannot scale to large problems. A new method for the minimization of the L_∞ norm is presented here, which provides a speedup of multiple orders of magnitude for data with high dimension. This method, termed Fast L_∞ Minimization, allows robust regression to be applied to a class of problems which were previously inaccessible. It is shown how the L_∞ norm minimization problem can be broken up into smaller sub-problems, which can then be solved extremely efficiently. Experimental results demonstrate the radical reduction in computation time, along with robustness against large numbers of outliers in a few model-fitting problems.
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