Budget Semi-supervised Learning

Authors:
Zhi-Hua Zhou;Michael Ng;Qiao-Qiao She;Yuan Jiang
Affiliations:
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 210093;Institute for Computational Mathematics, Hong Kong Baptist University, Hong Kong, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 210093;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 210093
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

In this paper we propose to study budget semi-supervised learning , i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data. This setting is with practical importance because in most real scenarios although there may exist abundant unlabeled data, the computational resource that can be used is generally not unlimited. Effective budget semi-supervised learning algorithms should be able to adjust behaviors considering the given resource budget. Roughly, the more resource, the more exploitation on unlabeled data. As an example, in this paper we show that this is achievable by a simple yet effective method.