Exploring features for complicated objects: cross-view feature selection for multi-instance learning

Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang

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

4 Citations (Scopus)


In traditional multi-instance learning (MIL), instances are typically represented by using a single feature view. As MIL becoming popular in domain specific learning tasks, aggregating multiple feature views to represent multi-instance bags has recently shown promising results, mainly because multiple views provide extra information for MIL tasks. Nevertheless, multiple views also increase the risk of involving redundant views and irrelevant features for learning. In this paper, we formulate a new cross-view feature selection problem that aims to identify the most representative features across all feature views for MIL. To achieve the goal, we design a new optimization problem by integrating both multiview representation and multi-instance bag constraints. The solution to the objective function will ensure that the identified top-m features are the most informative ones across all feature views. Experiments on two real-world applications demonstrate the performance of the cross-view feature selection for content-based image retrieval and social media content recommendation.

Original languageEnglish
Title of host publicationProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
EditorsMinos Garofalakis, Ian Soboroff, Torsten Suel, Min Wang
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450325981
Publication statusPublished - 2014
Externally publishedYes
EventACM International Conference on Information and Knowledge Management 2014 - Shanghai, China
Duration: 3 Nov 20147 Nov 2014
Conference number: 23rd


ConferenceACM International Conference on Information and Knowledge Management 2014
Abbreviated titleCIKM 2014
Internet address


  • Cross-view feature selection
  • Multi-instance learning

Cite this