Multi-view Proximity Learning for Clustering

  • Kun-Yu Lin
  • Ling Huang
  • Chang-Dong WangEmail author
  • Hong-Yang Chao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)


In recent years, multi-view clustering has become a hot research topic due to the increasing amount of multi-view data. Among existing multi-view clustering methods, proximity-based method is a typical class and achieves much success. Usually, these methods need proximity matrices as inputs, which can be constructed by some nearest-neighbors-based approaches. However, in this way, neither the intra-view cluster structure nor the inter-view correlation is considered in constructing proximity matrices. To address this issue, we propose a novel method, named multi-view proximity learning. By introducing the idea of representative, our model can consider both the relations between data objects and the cluster structure within individual views. Besides, the spectral-embedding-based scheme is adopted for modeling the correlations across different views, i.e. the view consistency and complement properties. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method.


Multi-view clustering Proximity learning Representative Spectral embedding 



This work was supported by NSFC (61502543), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Kun-Yu Lin
    • 1
  • Ling Huang
    • 1
  • Chang-Dong Wang
    • 1
    Email author
  • Hong-Yang Chao
    • 1
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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