Fast Multi-Instance Multi-Label Learning

Authors

  • Sheng-Jun Huang Nanjing University
  • Wei Gao Nanjing University
  • Zhi-Hua Zhou Nanjing University

DOI:

https://doi.org/10.1609/aaai.v28i1.8970

Keywords:

multi-instance multi-label learning, fast, key instance

Abstract

In multi-instance multi-label learning (MIML), one object is represented by multiple instances and simultaneously associated with multiple labels. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less; particularly, on a data set with 30K bags and 270K instances, where none of existing approaches can return results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output semantics.

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Published

2014-06-21

How to Cite

Huang, S.-J., Gao, W., & Zhou, Z.-H. (2014). Fast Multi-Instance Multi-Label Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8970

Issue

Section

Main Track: Novel Machine Learning Algorithms