Multiple instance learning via distance metric optimization

Abstract

Multiple Instance Learning (MIL) has been widely applied in practice, such as drug activity prediction, content-based image retrieval. In MIL, a sample, comprised of a set of instances, is called a bag. Labels are assigned to bags instead of instances. The uncertainty of labels on instances makes MIL different from conventional supervised single instance learning (SIL) tasks. Therefore, it is critical to learn an effective mapping to convert an MIL task to an SIL task. In this paper, we present OptMILES by learning the optimal transformation on the bag-to-instance similarity measure, exploring the optimal distance metric between instances, by an alternating minimization training procedure. We thoroughly evaluate the proposed method on both a synthetic dataset and real world datasets by comparing with representative MIL algorithms. The experimental results suggest the effectiveness of OptMILES.