Combining Label Information and Neighborhood Graph for Semi-supervised Learning

  • Lianwei Zhao
  • Siwei Luo
  • Mei Tian
  • Chao Shao
  • Hongliang Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, we consider the problem of combining the labeled and unlabeled examples to boost the performance of semi-supervised learning. We first define the label information graph, and then incorporate it with neighborhood graph. We propose a new regularized semi-supervised classification algorithm, in which the regularization term is based on this modified Graph Laplacian. According to the properties of Reproducing Kernel Hilbert Space (RKHS), the representer theorem holds, so the solution can be expressed by the Mercer kernel of examples. Experimental results show that our algorithm can use unlabeled and labeled examples effectively.


Regularization Term Neural Information Processing System Reproduce Kernel Hilbert Space Label Information Neighborhood Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lianwei Zhao
    • 1
  • Siwei Luo
    • 1
  • Mei Tian
    • 1
  • Chao Shao
    • 1
  • Hongliang Ma
    • 2
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.General Logistics DepartmentLogistics Science Research InstituteBeijingChina

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