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Article overview
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Multivariate Spearman's rho for aggregating ranks using copulas | | Justin Bedo
; Cheng Soon Ong
; | | Date: |
16 Oct 2014 | | Abstract: | We study the problem of rank aggregation: given a set of ranked lists, we
want to form a consensus ranking. Furthermore, we consider the case of extreme
lists: i.e., only the rank of the best or worst elements are known. We impute
missing ranks by the average value and generalise Spearman’s
ho to extreme
ranks. Our main contribution is the derivation of a non-parametric estimator
for rank aggregation based on multivariate extensions of Spearman’s
ho, which
measures correlation between a set of ranked lists. Multivariate Spearman’s
ho is defined using copulas, and we show that the geometric mean of
normalised ranks maximises multivariate correlation. Motivated by this, we
propose a weighted geometric mean approach for learning to rank which has a
closed form least squares solution. When only the best or worst elements of a
ranked list are known, we impute the missing ranks by the average value,
allowing us to apply Spearman’s
ho. Finally, we demonstrate good performance
on the rank aggregation benchmarks MQ2007 and MQ2008. | | Source: | arXiv, 1410.4391 | | Services: | Forum | Review | PDF | Favorites | |
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