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FSMRank: Feature Selection Algorithm for Learning to Rank

机译：FSMRank：用于学习排名的特征选择算法

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摘要

In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate $O({1}/{T^{2}})$. We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.
机译：近年来，人们对学习排名越来越感兴趣。已经证明将特征选择引入不同的学习问题中是有效的。这些事实促使我们研究用于学习排名的特征选择问题。我们提出了一种联合凸优化公式，该公式可最大程度地降低排名误差，同时进行特征选择。该优化公式提供了一个灵活的框架，在其中我们可以轻松地合并各种重要度度量和特征相似度度量。为了解决此优化问题，我们使用Nesterov方法导出具有快速收敛速率$ O（{1} / {T ^ {2}}）$的加速梯度算法。我们使用Rademacher复杂度进一步为所提出的优化问题开发了一个泛化界。在公共LETOR基准数据集上进行了广泛的实验评估。结果表明，所提出的方法显示：1）与用于排名的多个特征选择基线相比，具有显着的排名性能提高，以及2）与几种最新的按等级学习算法相比，具有非常好的竞争性能。

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