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Infinite push: a new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list

Agarwal, Shivani (2011) Infinite push: a new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list. In: SDM 2011, Proceedings of the Eleventh SIAM International Conference on Data Mining, April 28-30, 2011, Mesa, Arizona.

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Official URL: http://drona.csa.iisc.ernet.in/~shivani/Publicatio...

Abstract

Ranking problems have become increasingly important in machine learning and data mining in recent years, with applications ranging from information retrieval and recommender systems to computational biology and drug discovery. In this paper, we describe a new ranking algorithm that directly maximizes the number of relevant objects retrieved at the absolute top of the list. The algorithm is a support vector style algorithm, but due to the different objective, it no longer leads to a quadratic programming problem. Instead, the dual optimization problem involves l1, ∞ constraints; we solve this dual problem using the recent l1, ∞ projection method of Quattoni et al (2009). Our algorithm can be viewed as an l∞-norm extreme of the lp-norm based algorithm of Rudin (2009) (albeit in a support vector setting rather than a boosting setting); thus we refer to the algorithm as the ‘Infinite Push’. Experiments on real-world data sets confirm the algorithm’s focus on accuracy at the absolute top of the list.

Item Type: Conference Paper
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Additional Information: Copyright of this article belongs to Society for Industrial and Applied Mathematics.
Keywords: Ranking;Support Vector machines (SVMs);Area under ROC curve (AUC);l1, projections
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 19 Mar 2013 10:01
Last Modified: 19 Mar 2013 10:01
URI: http://eprints.iisc.ernet.in/id/eprint/46045

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