Semi-Supervised Domain Adaptation with Non-Parametric Copulas

A new framework based on the theory of copulas is proposed to address semi-supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate copula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Importantly, we introduce a novel vine copula model, which allows for this factorization in a nonparametric manner. Experimental results on regression problems with real-world data illustrate the efficacy of the proposed approach when compared to state-of-the-art techniques. 1 Introduction When humans address a new learning problem, they often use knowledge acquired while learning different but related tasks in the past. For example, when learning a second language, people rely on grammar rules and word derivations from their mother tongue. This is called language transfer [19]. However, in machine learning, most of the traditional methods are not able to exploit similarities between different learning tasks.