P. Trespeuch, Y. Fournier, et al.
Civil-Comp Proceedings
In multiple instance learning problems, patterns are often given as bags and each bag consists of some instances. Most of existing research in the area focuses on multiple instance classification and multiple instance regression, while very limited work has been conducted for multiple instance clustering (MIC). This paper formulates a novel framework, maximum margin multiple instance clustering (M3 IC), for MIC. However, it is impractical to directly solve the optimization problem of M3IC. Therefore, M3 IC is relaxed in this paper to enable an efficient optimization solution with a combination of the constrained concave-convex procedure and the cutting plane method. Furthermore, this paper presents some important properties of the proposed method and discusses the relationship between the proposed method and some other related ones. An extensive set of empirical results are shown to demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency. © 2011 IEEE.
P. Trespeuch, Y. Fournier, et al.
Civil-Comp Proceedings
Arnold.L. Rosenberg
Journal of the ACM
Annina Riedhauser, Viacheslav Snigirev, et al.
CLEO 2023
Hironori Takeuchi, Tetsuya Nasukawa, et al.
Transactions of the Japanese Society for Artificial Intelligence