Multiple instance learning with bag dissimilarities

V. Cheplygina, D.M.J. Tax, M. Loog

Research output: Contribution to journalArticleAcademicpeer-review

62 Citations (Scopus)

Abstract

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, but do not generalize to the whole range of MIL problems. Other MIL methods shift the focus of assumptions from the labels to the overall (dis)similarity of bags, and therefore learn from bags directly. We propose to represent each bag by a vector of its dissimilarities to other bags in the training set, and treat these dissimilarities as a feature representation. We show several alternatives to define a dissimilarity between bags and discuss which definitions are more suitable for particular MIL problems. The experimental results show that the proposed approach is computationally inexpensive, yet very competitive with state-of-the-art algorithms on a wide range of MIL datasets.
Original languageEnglish
Pages (from-to)264-275
Number of pages12
JournalPattern Recognition
Volume48
Issue number1
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Dissimilarity representation
  • Drug activity prediction
  • Image classification
  • Multiple instance learning
  • Point set distance
  • Text categorization

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