Fogel, Fajwel, d'Aspremon, Alexandre and Vojnovic, Milan 
ORCID: 0000-0003-1382-022X
(2014)
SerialRank: spectral ranking using seriation.
In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D. and Weinberger., K.Q., (eds.)
Advances in Neural Information Processing Systems 27.
Curran Associates, Inc., pp. 900-908.
Abstract
We describe a seriation algorithm for ranking a set of n items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder this matrix and construct a ranking. We first show that this spectral seriation algorithm recovers the true ranking when all pairwise comparisons are observed and consistent with a total order. We then show that ranking reconstruction is still exact even when some pairwise comparisons are corrupted or missing, and that seriation based spectral ranking is more robust to noise than other scoring methods. An additional benefit of the seriation formulation is that it allows us to solve semi-supervised ranking problems. Experiments on both synthetic and real datasets demonstrate that seriation based spectral ranking achieves competitive and in some cases superior performance compared to classical ranking methods.
| Item Type: | Book Section |
|---|---|
| Official URL: | https://papers.nips.cc/book/advances-in-neural-inf... |
| Additional Information: | © 2014 The Authors |
| Divisions: | Statistics |
| Subjects: | H Social Sciences > HA Statistics |
| Date Deposited: | 23 Nov 2017 12:58 |
| Last Modified: | 06 Oct 2024 07:30 |
| URI: | http://eprints.lse.ac.uk/id/eprint/85714 |
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