Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling

E. Morvant, Pattern Recognition Letters 51 (2014) 37–43.


Journal Article | Published | English
Abstract
In machine learning, the domain adaptation problem arrives when the test (tar-get) 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-valued 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 learn-ing 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 tar-get 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.
Publishing Year
Date Published
2014-10-01
Journal Title
Pattern Recognition Letters
Volume
51
Page
37-43
IST-REx-ID

Cite this

Morvant E. Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling. Pattern Recognition Letters. 2014;51:37-43. doi:10.1016/j.patrec.2014.08.013
Morvant, E. (2014). Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling. Pattern Recognition Letters, 51, 37–43. https://doi.org/10.1016/j.patrec.2014.08.013
Morvant, Emilie. “Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling.” Pattern Recognition Letters 51 (2014): 37–43. https://doi.org/10.1016/j.patrec.2014.08.013.
E. Morvant, “Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling,” Pattern Recognition Letters, vol. 51, pp. 37–43, 2014.
Morvant E. 2014. Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling. Pattern Recognition Letters. 51, 37–43.
Morvant, Emilie. “Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling.” Pattern Recognition Letters, vol. 51, Elsevier, 2014, pp. 37–43, doi:10.1016/j.patrec.2014.08.013.

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