TY - JOUR
T1 - A fast algorithm for learning large scale preference relations
JF - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Y1 - 2007
A1 - Raykar,V.C.
A1 - Duraiswami, Ramani
A1 - Krishnapuram,B.
AB - We consider the problem of learning the rank-ing function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on training data. Relying on an ϵ-exact approx- imation for the error-function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learn- ing ranking functions from O(m2), to O(m), where m is the size of the training data. Experiments on public benchmarks for ordi- nal regression and collaborative filtering show that the proposed algorithm is as accurate as the best available methods in terms of rank- ing accuracy, when trained on the same data, and is several orders of magnitude faster.
VL - 2
ER -