Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000131-6.doi: 10.11896/jsjkx.211000131

• Artificial Intelligence • Previous Articles     Next Articles

Multi-view Distance Metric Learning with Inter-class and Intra-class Density

REN Shuang-yan1, GUO Wei1, FAN Chang-qi2, WANG Zhe1, WU Song-yang3   

  1. 1 School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
    2 Shanghai Mobile Internet Industry Promotion Center,Shanghai 200333,China
    3 The Third Research Institute of Ministry of Public Security,Shanghai 201204,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:REN Shuang-yan,born in 1997,postgraduate.Her main research interests include pattern recognition and machine learning.
    WANG Zhe,born in 1981,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include pattern recognition and image processing.
  • Supported by:
    Shanghai Science and Technology Program(20511100600),Natural Science Foundation of China(62076094) and Key Lab of Information Network Security of Ministry of Public Security(The Third Research Institute ofMinistry of Public Security)(C20603).

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Abstract: Geometric information can provide prior knowledge and intuitive explanation for classification methods.Observing samples from geometric perspective is a novel method of sample learning,and density is a very intuitive form of geometric information.This paper proposes a multi-view distance metric learning method with inter-class and intra-class density to learn a metric space.In this space,the heterogeneous samples are more scattered,and the homogeneous samples are closer.First,the inter-class density is introduced under the large margin framework,and the samples in the metric space are constrained by minimizing the inter-class density,so as to realize the inter-class dispersion and improve the classification performance.Second,maximize the intra-class density to achieve the effect of similar samples close to each other,so as to achieve intra-class compactness.Finally,to better mine the complementary information of the multi-view samples,the correlation between the views in the metric space is maximized,so that the views can learn from each other adaptively and explore the complementary information among the views.A large number of experimental results on real-world datasets demonstrate the superiority of the proposed method.

Key words: Geometric information, Inter-class density, Intra-class density, Complementary information, View correlation

CLC Number: 

  • TP181
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