Semi-supervised Ranking Pursuit

Evgeni Tsivtsivadze; Tom Heskes

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arXiv · 1307.0846

Semi-supervised Ranking Pursuit

Evgeni Tsivtsivadze·Tom Heskes

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Abstract

We propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on pairs of data points, and is a generalization of the kernel matching pursuit method. It can operate both in a supervised and a semi-supervised setting and allows efficient search for multiple, near-optimal solutions. Furthermore, we describe the extension of the algorithm suitable for combined ranking and regression tasks. In our experiments we demonstrate that the proposed algorithm outperforms several state-of-the-art learning methods when taking into account unlabeled data and performs comparably in a supervised learning scenario, while providing sparser solutions.

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BibTeX RIS

Evgeni Tsivtsivadze, Tom Heskes. 2013-07-02. Semi-supervised Ranking Pursuit. https://arxiv.org/abs/1307.0846

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