Distributed Least Square Ranking with Random Features

Rong Yin, Yong Liu, Weiping Wang, Dan Meng

Keywords: least square ranking, distributed learning, learning theory, random features

TL;DR: We study the statistical properties of pairwise ranking using distributed learning and random features, establish the convergence rate in probability, and demonstrate the power of the proposed methods via numerical experiments.

Abstract: In this paper, we study the statistical properties of pairwise ranking using distributed learning and random features (called DRank-RF) and establish its convergence analysis in probability. Theoretical analysis shows that DRank-RF remarkably reduces the computational requirements while preserving a satisfactory convergence rate. An extensive experiment verifies the effectiveness of DRank-RF. Furthermore, to improve the learning performance of DRank-RF, we propose an effective communication strategy for it and demonstrate the power of communications via theoretical assessments and numerical experiments.

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