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

ACM SE '10 Pub Date : 2010-04-15 DOI:10.1145/1900008.1900052
Xiaofei Nan, Yixin Chen, Xin Dang, D. Wilkins
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引用次数: 5

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.
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学习使用1范数正则化和凸包约简进行排序
排序问题出现在许多研究领域,如顾客评价、社会科学、经济学和信息检索。当考虑成对数据时,排名可以被表述为一个分类问题。然而,这种方法增加了问题的复杂性,从线性到二次的样本量。在本文中,我们提出了一种减少凸包的方法来减少这种影响。我们还提出了一种1范数正则化方法来同时寻找线性排序函数和执行特征子集选择。所提出的方法是一个线性规划。本文给出了在人工数据和两个真实数据集(混凝土抗压强度数据集和鲍鱼数据集)上的实验结果。
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