Joint Gaussian Based Measures for Multiple-Instance Learning

As an actively investigated topic in machine learning, Multiple-Instance Learning (MIL) has many proposed solutions, including both supervised and unsupervised methods. Most of these solutions are restricted to the original assumption that comes with the notion of MIL: the label of a multiple-instance object is directly determined by the labels of its instances. However, this assumption faces adverse circumstances when there is no clear relation between the over-all label and the labels of instances. Most previous approaches avoid this problem in practice by taking each multiple-instance object as a whole instead of starting with learning in instance spaces, but they either lose information or are time consuming. In this paper, we introduce two joint Gaussian based measures for MIL, Joint Gaussian Similarity (JGS) and Joint Gaussian Distance (JGD), which require no prior knowledge of relations between the labels of multiple-instance objects and their instances. JGS is a measure of similarity while JGD is a metric of which the properties are necessary for many techniques like clustering and embedding. JGS and JGD take all the information into account and many traditional machine learning methods can be introduced to MIL. Extensive experimental evaluations on various real-world data demonstrate the effectiveness of both measures, and better performances than state-of-the-art MIL algorithms on benchmark tasks.