Semiparametric preference learning

Abstract

Unlike traditional supervised learning problems, preference learning learns from data available in the form of pairwise preference relations between instances. Existing preference learning methods are either parametric or nonparametric in nature. We propose in this paper a semiparametric preference learning model, abbreviated as SPPL, with the aim of combining the strengths of the parametric and nonparametric approaches. SPPL uses multiple Gaussian processes which are linearly coupled to determine the preference relations between instances. SPPL is more powerful than previous models while keeping the computational complexity low (linear in the number of distinct instances). We devise an efficient algorithm for model learning. Empirical studies have been conducted on two real-world data sets showing that SPPL outperforms related preference learning methods. © 2014 Tsinghua University Press.