Learning to Rank Using 1-norm Regularization and Convex Hull Reduction

The ranking problem appears in many areas of study such as customer rating, social science, economics, and information retrieval. Ranking can be formulated as a classification problem when pair-wise data is considered. However this approach increases the problem complexity from linear to quadratic in terms of sample size. We present in this paper a convex hull reduction method to reduce this impact. We also propose a 1-norm regularization approach to simultaneously find a linear ranking function and to perform feature subset selection. The proposed method is formulated as a linear program. We present experimental results on artificial data and two real data sets, concrete compressive strength data set and Abalone data set.