Multi-view Similarity Learning of Manifold Data

  • Rui-rui Wang
  • Si-bao ChenEmail author
  • Bin Luo
  • Jian Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)


In recent years, multi-view learning methods have developed rapidly where graph-based approaches have achieved good performance. Usually, these learning methods construct information graph for each view or fuse different views into one graph. In this paper, a novel multi-view learning model that learns one similarity matrix for all views named Multi-view Similarity Learning (MSL) is proposed, where adaptive weights are learned for each view. The multi-view similarity learning method is further extended to kernel space. Experiments of classification, clustering and semi-supervised classification on different real-world datasets show the effectiveness of the proposed method.


Laplacian Eigenmaps Multi-view learning Similarity learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rui-rui Wang
    • 1
  • Si-bao Chen
    • 1
    • 2
    Email author
  • Bin Luo
    • 1
  • Jian Zhang
    • 2
  1. 1.Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.Peking University Shenzhen InstituteShenzhenChina

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