%%% -*-BibTeX-*-
@article{Shen2014Outlier,
    author = {Fumin Shen and Chunhua Shen  and Rhys Hill  and
                Anton {van den Hengel} and Zhenmin Tang},
    title  = {Fast approximate $L_\infty$ minimization: {S}peeding up robust regression},
    journal= {Computational Statistics and Data Analysis},
    volume = {77},
    number = {},
    year   = {2014},
    month  = {September},
    pages  = {25--37},
    eprint = {1304.1250},
    venue  = {CSDA},
    note   = {},
    abstract={
    Minimization of the $L_\infty$ 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 $L_\infty$  norm minimization are
    slow, and therefore cannot scale to large problems. A new method for the
    minimization of the $L_\infty$ norm is presented here, which provides a speedup of
    multiple orders of magnitude for data with high dimension. This method,
    termed Fast $L_\infty$  Minimization, allows robust regression to be applied to a class
    of problems which were previously inaccessible. It is shown how the $L_\infty$ 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.
    },
}