Label Distribution Learning by Exploiting Label Correlations


  • Xiuyi Jia Nanjing University of Science and Technology
  • Weiwei Li Nanjing University of Aeronautics and Astronautics
  • Junyu Liu Nanjing University of Science and Technology
  • Yu Zhang East China University of Science and Technology


Label distribution learning


Label distribution learning (LDL) is a newly arisen machine learning method that has been increasingly studied in recent years. In theory, LDL can be seen as a generalization of multi-label learning. Previous studies have shown that LDL is an effective approach to solve the label ambiguity problem. However, the dramatic increase in the number of possible label sets brings a challenge in performance to LDL. In this paper, we propose a novel label distribution learning algorithm to address the above issue. The key idea is to exploit correlations between different labels. We encode the label correlation into a distance to measure the similarity of any two labels. Moreover, we construct a distance-mapping function from the label set to the parameter matrix. Experimental results on eight real label distributed data sets demonstrate that the proposed algorithm performs remarkably better than both the state-of-the-art LDL methods and multi-label learning methods.




How to Cite

Jia, X., Li, W., Liu, J., & Zhang, Y. (2018). Label Distribution Learning by Exploiting Label Correlations. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from