Distributed Least Square Ranking with Random Features

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.