Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning


  • Cam-Tu Nguyen Nanjing University
  • Xiaoliang Wang Nanjing University
  • Jing Liu Institute of Automation Chinese Academy of Sciences
  • Zhi-Hua Zhou Nanjing University



multi-view, multi-instance, multi-label learning


Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference. The model can naturally deal with examples in which some views could be absent (partial examples). On multi-view datasets, it is shown that our method is better than other multi-view and single-view approaches particularly in the presence of partial examples. On single-view benchmarks, extensive evaluation shows that our method is highly competitive or better than other MIML approaches on labeling examples and instances. Moreover, our method can effectively handle datasets with a large number of labels.




How to Cite

Nguyen, C.-T., Wang, X., Liu, J., & Zhou, Z.-H. (2014). Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Novel Machine Learning Algorithms