Multiple-Instance Learning from Pairwise Comparison Bags

doi:10.1145/3696460

Multiple-Instance Learning from Pairwise Comparison Bags

2024-12-14

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DOI：10.1145/3696460

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Multiple-instance learning (MIL) is a significant weakly supervised learning problem, where the training data consist of bags containing multiple instances and bag-level labels. Most previous MIL research required fully labeled bags. However, collecting such data is challenging due to the labeling costs or privacy concerns. Fortunately, we can easily collect pairwise comparison information, indicating one bag is more likely to be positive than the other. Therefore, we investigate a novel MIL problem about learning a bag-level binary classifier only from pairwise comparison bags. To solve this problem, we display the data generation process and provide a baseline method to train an instance-level classifier based on unlabeled-unlabeled learning. To achieve better performance, we propose a convex formulation to train a bag-level classifier and give a generalization error bound. Comprehensive experiments show that both the baseline method and the convex formulation achieve satisfactory performance, while the convex formulation performs better.1

Keywords:

multiple-instance learning

empirical risk minimization

convex formulation

Multiple-Instance Learning from Pairwise Comparison Bags

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