Semi-Supervised Domain Adaptation with Non-Parametric Copulas
Machine Learning
2013-01-03 v1 Machine Learning
Abstract
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 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.
Cite
@article{arxiv.1301.0142,
title = {Semi-Supervised Domain Adaptation with Non-Parametric Copulas},
author = {David Lopez-Paz and José Miguel Hernández-Lobato and Bernhard Schölkopf},
journal= {arXiv preprint arXiv:1301.0142},
year = {2013}
}
Comments
9 pages, Appearing on Advances in Neural Information Processing Systems 25