%0 Journal Article %J CVGIP: Graphical Models and Image Processing %D 1994 %T Computationally Efficient Algorithms for High-Dimensional Robust Estimators %A Mount, Dave %A Netanyahu,N. S %X Given a set of n distinct points in d-dimensional space that are hypothesized to lie on a hyperplane, robust statistical estimators have been recently proposed for the parameters of the model that best fits these points. This paper presents efficient algorithms for computing median-based robust estimators (e.g., the Theil-Sen and repeated median (RM) estimators) in high-dimensional space. We briefly review basic computational geometry techniques that were used to achieve efficient algorithms in the 2-D case. Then generalization of these techniques to higher dimensions is introduced. Geometric observations are followed by a presentation of O(nd − 1 log n) expected time algorithms for the d-dimensional Theil-Sen and RM estimators. Both algorithms are space optimal; i.e., they require O(n) storage, for fixed d. Finally, an extension of the methodology to nonlinear domain(s) is demonstrated. %B CVGIP: Graphical Models and Image Processing %V 56 %P 289 - 303 %8 1994/07// %@ 1049-9652 %G eng %U http://www.sciencedirect.com/science/article/pii/S1049965284710261 %N 4 %R 10.1006/cgip.1994.1026