Large Margin Metric Learning for Multi-Label Prediction


  • Weiwei Liu University of Technology, Sydney
  • Ivor Tsang University of Technology, Sydney



Multi-label prediction,Metric Learning,$k$ Nearest Neighbors


Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unacceptable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. In particular, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods.




How to Cite

Liu, W., & Tsang, I. (2015). Large Margin Metric Learning for Multi-Label Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



Main Track: Novel Machine Learning Algorithms