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Multiple-Instance Learning from Pairwise Comparison Bags
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AI
DOI：10.1145/3696460.png)
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摘要
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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
Keyword:
multiple-instance learning
empirical risk minimization
convex formulation
期刊
IF:
6.6
论文数:
1.5K
被引数:
6.2K
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引用论文
Multiple instance learning: A survey of problem characteristics and applications多实例学习: 问题特征与应用综述.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏Distance-based positive and unlabeled learning for ranking基于距离的正面和无标签学习的排名PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
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