Correcting Sample Selection Bias by Unlabeled Data

We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appro- priate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estima- tion. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.

Authors

• Jiayuan Huang UCL Hawkes Institute, University College London, UK Dept of Medical Physics & Biomedical Engineering, University College London, UK Visual Understanding Research Group, Dept of Informatics, King’s College London, UK
• Arthur Gretton University College London
• Karsten Borgwardt
• Bernhard Schölkopf
• Alex Smola

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