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Sample complexity of rank regression using pairwise comparisons
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DOI：10.1016/j.patcog.2022.108688.png)
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Abstract
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We consider a rank regression setting, in which a dataset of N samples with features in R-d is ranked by an oracle via M pairwise comparisons. Specifically, there exists a latent total ordering of the samples; when presented with a pair of samples, a noisy oracle identifies the one ranked higher with respect to the underlying total ordering. A learner observes a dataset of such comparisons and wishes to regress sample ranks from their features. We show that to learn the model parameters with epsilon > 0 accuracy, it suffices to conduct M is an element of Omega(dN log(3) N/epsilon(2)) comparisons uniformly at random when N is Omega(d/epsilon(2)). (C) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Sample complexity
Rank regression
Pairwise comparisons
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