Multi-instance transfer metric learning by weighted distribution and consistent maximum likelihood estimation

Siyu Jiang, Yonghui Xu*, Hengjie Song, Qingyao Wu, Kwok Po NG, Huaqing Min, Shaojian Qiu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

7 Citations (Scopus)

Abstract

Multi-Instance learning (MIL) aims to predict labels of unlabeled bags by training a model with labeled bags. The usual assumption of existing MIL methods is that the underlying distribution of training data is the same as that of the testing data. However, this assumption may not be valid in practice, especially when training data from a source domain and testing data from a target domain are drawn from different distributions. In this paper, we put forward a novel algorithm Multi-Instance Transfer Metric Learning (MITML). Specially, MITML first attempts to bridge the distributions of different domains by using the bag weighting method. Then a consistent maximum likelihood estimation method is learned to construct an optimal distance metric and exploited to classify testing bags. Comprehensive experimental results on benchmark datasets have demonstrated that the learning performance of the proposed MITML algorithm is better than those of other state-of-the-art MIL algorithms.

Original languageEnglish
Pages (from-to)49-60
Number of pages12
JournalNeurocomputing
Volume321
DOIs
Publication statusPublished - 10 Dec 2018

Scopus Subject Areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

User-Defined Keywords

  • Bag weights estimation
  • Consistent maximum likelihood estimation
  • Metric learning
  • Multi-instance learning
  • Transfer learning

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