Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric

Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo, Thuong Nguyen

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

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