Semi-supervised Classification Based on Smooth Graphs

  • Xueyuan Zhou
  • Chunping Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)


In semi-supervised classification, labels smoothness and cluster assumption are the key point of many successful methods. In graph-based semi-supervised classification, graph representations of the data are quite important. Different graph representations can affect the classification results greatly. Considering the two assumptions and graph representations, we propose a novel method to build a better graph for semi-supervised classification. The graph in our method is called m-step Markov random walk graph (mMRW graph). The smoothness of this graph can be controlled by a parameter m. We believe that a relatively much smoother graph will benefit transductive learning. We also discuss some benefits brought by our smooth graphs. A cluster cohesion based parameter learning method can be efficiently applied to find a proper m. Experiments on artificial and real world dataset indicate that our method has a superior classification accuracy over several state-of-the-art methods.


Random Walk Unlabeled Data Neural Information Processing System Connection Matrix Cluster Kernel 
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

  • Xueyuan Zhou
    • 1
  • Chunping Li
    • 1
  1. 1.School of softwareTsinghua UniversityBeijingP.R. China

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