Robust Linear and Support Vector Regression
Abstract
The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is
modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear and
nonlinear support vector estimators. Previous models were significantly more complex or formulated in the dual space and most
involved specialized numerical algorithms for solving the robust Huber linear estimator [3], [6], [12], [13], [14], [23], [28]. Numerical test
comparisons with these algorithms indicate the computational effectiveness of the new quadratic programming model for both linear
and nonlinear support vector problems. Results are shown on problems with as many as 20,000 data points, with considerably faster
running times on larger problems.
Subject
kernel methods
Huber M-estimator
regression
support vector machines
Permanent Link
http://digital.library.wisc.edu/1793/64276Citation
99-09