MILES: Multiple-Instance Learning via Embedded Instance Selection

Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (Multiple-Instance Learning via Embedded instance Selection), which converts the multipleinstance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or ir...

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Yixin Chen, Jinbo Bi, James Ze Wang
• Keywords: Activity Prediction Problem, Instance Similarity Measure, Labels, PAMI 2006