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. Categories and Subject Descriptors L.2 [Learning]: Strategic Aspects of eLearning General Terms Theory Keywords Ranking, SVM, Convex Hull

• Type: Journal
• Year: 2010
• Where: ACMSE
• Authors: Xiaofei Nan, Yixin Chen, Xin Dang, Dawn Wilkins
• Keywords: ACMSE 2010, Convex Hull, Linear Ranking Function, Ranking, Theoretical Computer Science