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Multiple instance data are sets or multi-sets of unordered elements. Usingmetrics or distances for sets, we propose an approach to several multipleinstance learning tasks, such as clustering (unsupervised learning),classification (supervised learning), and novelty detection (semi-supervisedlearning). In particular, we introduce the Optimal Sub-Pattern Assignmentmetric to multiple instance learning so as to provide versatile design choices.Numerical experiments on both simulated and real data are presented toillustrate the versatility of the proposed solution.

Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric