Generalized Optimization Framework for Graph-based Semi-supervised Learning

  title={Generalized Optimization Framework for Graph-based Semi-supervised Learning},
  author={Konstantin Avrachenkov and Paulo Gonçalves and Alexey Mishenin and Marina Sokol},
We develop a generalized optimization framework for graphbased semi-supervised learning. The framework gives as particular cases the Standard Laplacian, Normalized Laplacian and PageRank based methods. We have also provided new probabilistic interpretation based on random walks and characterized the limiting behaviour of the methods. The random walk based interpretation allows us to explain differences between the performances of methods with different smoothing kernels. It appears that the… CONTINUE READING
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