Domain adaptation of weighted majority votes via perturbed variation-based self-labeling

Emilie Morvant (1)

1 LHC - Laboratoire Hubert Curien

In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to generalize on a new distribution, for which we have no label information. We focus on learning classification models defined as a weighted majority vote over a set of real-val ued functions. In this context, Germain et al. (2013) have shown that a measure of disagreement between these functions is crucial to control. The core of this measure is a theoretical bound--the C-bound (Lacasse et al., 2007)--which involves the disagreement and leads to a well performing majority vote learning algorithm in usual non-adaptative supervised setting: MinCq. In this work, we propose a framework to extend MinCq to a domain adaptation scenario. This procedure takes advantage of the recent perturbed variation divergence between distributions proposed by Harel and Mannor (2012). Justified by a theoretical bound on the target risk of the vote, we provide to MinCq a target sample labeled thanks to a perturbed variation-based self-labeling focused on the regions where the source and target marginals appear similar. We also study the influence of our self-labeling, from which we deduce an original process for tuning the hyperparameters. Finally, our framework called PV-MinCq shows very promising results on a rotation and translation synthetic problem.

Mots clés: Machine learning; Classification; Domain adaptation; Majority vote; PAC-Bayes

Domaines: Machine Learning [stat.ML]

Emilie Morvant. Domain adaptation of weighted majority votes via perturbed variation-based self-labeling. Pattern Recognition Letters, 2015, 51, pp.37-43. ⟨10.1016/j.patrec.2014.08.013⟩. ⟨hal-01056599⟩