 Fast approximate L∞ minimization: Speeding up robust regressionFumin Shen, Chunhua Shen, Rhys Hill, Anton van den Hengel and Zhenmin Tang Computational Statistics & Data Analysis, 2014, vol. 77, issue C, 25-37 Abstract: 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 be scaled 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 was 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. Keywords: Least-squares regression; Outlier removal; Robust regression; Face recognition (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:77:y:2014:i:c:p:25-37 DOI: 10.1016/j.csda.2014.02.018 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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