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Graph Based Multi-class Semi-supervised Learning Using Gaussian Process

  • Yangqiu Song
  • Changshui Zhang
  • Jianguo Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)

Abstract

This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian process to solve this problem. We propose the prior based on the normalized graph Laplacian, and introduce a new likelihood based on softmax function model. Both the transductive and inductive problems are regarded as MAP (Maximum A Posterior) problems. Experimental results show that our method is competitive with the existing semi-supervised transductive and inductive methods.

Keywords

Gaussian Process Unlabeled Data Unseen Data Prediction Phase Graph Base Method 
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

  • Yangqiu Song
    • 1
  • Changshui Zhang
    • 1
  • Jianguo Lee
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Department of AutomationTsinghua UniversityBeijingChina

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