Learning to rank using 1-norm regularization and convex hull reduction

Authors:
Xiaofei Nan;Yixin Chen;Xin Dang;Dawn Wilkins
Affiliations:
University of Mississippi, University, MS;University of Mississippi, University, MS;University of Mississippi, University, MS;University of Mississippi, University, MS
Venue:
Proceedings of the 48th Annual Southeast Regional Conference
Year:
2010

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Abstract

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.