Direct discriminative bag mapping for multi-instance learning

Jia Wu, Shirui Pan, Peng Zhang, Xingquan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16 )
EditorsDale Schuurmans, Michael Wellman
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages2
ISBN (Electronic)9781577357605
Publication statusPublished - 2016
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2016 - Phoenix Convention Center, Phoenix, United States of America
Duration: 12 Feb 201617 Feb 2016
Conference number: 30th


ConferenceAAAI Conference on Artificial Intelligence 2016
Abbreviated titleAAAI 2016
CountryUnited States of America
Internet address

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