Multi-view Label Space Dimension Reduction

  • Qi Hu
  • Pengfei ZhuEmail author
  • Changqing Zhang
  • Qinghua Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)


In multi-label classification, the explosion of the label space makes the classic multi-label classification models computationally inefficient and degrades the classification performance. To alleviate the curse of dimensionality in label space, many label space dimension reduction (LSDR) algorithms have been developed in last few years. Whereas, they are all designed for single-view learning and ignore that one sample can be represented from different views. In this paper, we propose a multi-view LSDR model for multi-label classification. The weights of different views are learned and then multi-view label embedding results are combined by the learned weights. Experiments on benchmark datasets show that the proposed multi-view learning model outperforms the best single-view model and the majority voting method.


Multi-view learning Label embedding Dimension reduction 



This work was supported by the National Program on Key Basic Research Project under Grant 2013CB329304, the National Natural Science Foundation of China under Grants 61502332, 61432011, 61222210.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qi Hu
    • 1
  • Pengfei Zhu
    • 1
    Email author
  • Changqing Zhang
    • 1
  • Qinghua Hu
    • 1
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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