Multiple-Instance Learning Via Random Walk

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.

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: Dong Wang, Jianmin Li, Bo Zhang
• Keywords: Closed Form Solution, ECML 2006, Machine Learning, Supervised Learning Problem, Support Vector Machine