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Ranking and empirical minimization of U-statistics

Stéphan Clémençon·Gábor Lugosi·Nicolas Vayatis
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Abstract

The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. In this paper we formulate the ranking problem in a rigorous statistical framework. The goal is to learn a ranking rule for deciding, among two instances, which one is "better," with minimum ranking risk. Since the natural estimates of the risk are of the form of a U-statistic, results of the theory of U-processes are required for investigating the consistency of empirical risk minimizers. We establish in particular a tail inequality for degenerate U-processes, and apply it for showing that fast rates of convergence may be achieved under specific noise assumptions, just like in classification. Convex risk minimization methods are also studied.

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Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis. 2006-03-05. Ranking and empirical minimization of U-statistics. https://arxiv.org/abs/math/0603123

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