Correcting Sample Selection Bias by Unlabeled Data

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, Alex Smola


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.