Multiple-Instance learning via random walk

Authors:
Dong Wang;Jianmin Li;Bo Zhang
Affiliations:
State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China;State Key Laboratory of Intelligent Technology and System, Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China
Venue:
ECML'06 Proceedings of the 17th European conference on Machine Learning
Year:
2006

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Abstract

This paper presents a decoupled two stage solution to the multiple-instance learning (MIL) problem. With a constructed affinity matrix to reflect the instance relations, a modified Random Walk on a Graph process is applied to infer the positive instances in each positive bag. This process has both a closed form solution and an efficient iterative one. Combined with the Support Vector Machine (SVM) classifier, this algorithm decouples the inferring and training stages and converts MIL into a supervised learning problem. Compared with previous algorithms on several benchmark data sets, the proposed algorithm is quite competitive in both computational efficiency and classification accuracy.