Spectral Ranking with Covariates
Machine Learning
2022-04-07 v3 Machine Learning
Abstract
We consider spectral approaches to the problem of ranking n players given their incomplete and noisy pairwise comparisons, but revisit this classical problem in light of player covariate information. We propose three spectral ranking methods that incorporate player covariates and are based on seriation, low-rank structure assumption and canonical correlation, respectively. Extensive numerical simulations on both synthetic and real-world data sets demonstrated that our proposed methods compare favorably to existing state-of-the-art covariate-based ranking algorithms.
Keywords
Cite
@article{arxiv.2005.04035,
title = {Spectral Ranking with Covariates},
author = {Siu Lun Chau and Mihai Cucuringu and Dino Sejdinovic},
journal= {arXiv preprint arXiv:2005.04035},
year = {2022}
}