Direct Discriminative Bag Mapping for Multi-Instance Learning

Authors

  • Jia Wu University of Technology Sydney
  • Shirui Pan University of Technology Sydney
  • Peng Zhang University of Technology Sydney
  • Xingquan Zhu Florida Atlantic University

DOI:

https://doi.org/10.1609/aaai.v30i1.9918

Keywords:

Bag, Multi-instance, Classification

Abstract

Multi-instance learning (MIL) is useful for tackling labeling ambiguity in learning tasks, by allowing a bag of instances to share one label. Recently, bag mapping methods, which transform a bag to a single instance in a new space via instance selection, have drawn significant attentions. To date, most existing works are developed based on the original space, i.e., utilizing all instances for bag mapping, and instance selection is indirectly tied to the MIL objective. As a result, it is hard to guarantee the distinguish capacity of the selected instances in the new bag mapping space for MIL. In this paper, we propose a direct discriminative mapping approach for multi-instance learning (MILDM), which identifies instances to directly distinguish bags in the new mapping space. Experiments and comparisons on real-world learning tasks demonstrate the algorithm performance.

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Published

2016-03-05

How to Cite

Wu, J., Pan, S., Zhang, P., & Zhu, X. (2016). Direct Discriminative Bag Mapping for Multi-Instance Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9918