Multiple-Instance Learning from Similar and Dissimilar Bags

Abstract

Multiple-instance learning (MIL) is an important weakly supervised binary classification problem, where training instances are arranged in bags, and each bag is assigned a positive or negative label. Most of the previous studies for MIL assume that training bags are fully labeled. However, in some real-world scenarios, it could be difficult to collect fully labeled bags, due to the expensive time and labor consumption of the labeling task. Fortunately, it could be much easier for us to collect similar and dissimilar bags (indicating whether two bags share the same label or not), because we do not need to figure out the underlying label of each bag in this case. Therefore, in this paper, we for the first time investigate MIL from only similar and dissimilar bags. To solve this new MIL problem, we propose a convex formulation to train a bag-level classifier based on empirical risk minimization and theoretically derive a generalization error bound. In addition, we also propose a strong baseline for this new MIL problem, which aims to train an instance-level classifier by minimizing the instance-level empirical risk. Extensive experimental results clearly demonstrate that our proposed baseline works well, while our proposed convex formulation is even better.