Article

Correcting sample selection bias by unlabeled data

Authors:

Alexander J. Smola,

Arthur Gretton,

Karsten M. Borgwardt,

Bernhard ScholkopfAuthors Info & Claims

NIPS'06: Proceedings of the 20th International Conference on Neural Information Processing Systems

Pages 601 - 608

Published: 04 December 2006 Publication History

Abstract

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 appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.

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Published In

cover image Guide Proceedings

NIPS'06: Proceedings of the 20th International Conference on Neural Information Processing Systems

December 2006

1632 pages

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 04 December 2006

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Guan JWang JNiu WPeng ZWang SLiu ZZhou GRen B(2024)Towards Recognizing Food Types for Unseen SubjectsACM Transactions on Computing for Healthcare10.1145/36964246:1(1-21)Online publication date: 23-Sep-2024
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