Semi-Supervised Domain Adaptation with Non-Parametric Copulas [article]

David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf
2013 arXiv   pre-print
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 cop- ula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Impor- tantly, we introduce a novel vine copula model, which allows for this factorization in a non-parametric
more » ... Experimental results on regression problems with real-world data illustrate the efficacy of the proposed approach when compared to state-of-the-art techniques.
arXiv:1301.0142v1 fatcat:jyni2boikjhntosqwsxcux5ltq