Multiple-instance learning from unlabeled bags with pairwise similarity

• Main Authors: Feng, Lei, Shu, Senlin, Cao, Yuzhou, Tao, Lue, Wei, Hongxin, Xiang, Tao, An, Bo, Niu, Gang
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Multiple-Instance Learning; Empirical Risk Minimization
• Online Access: https://hdl.handle.net/10356/172864
• Institution: Nanyang Technological University

In multiple-instance learning (MIL), each training example is represented by a bag of instances. A training bag is either negative if it contains no positive instances or positive if it has at least one positive instance. Previous MIL methods generally assume that training bags are fully labeled. Ho...