Generalized Optimization Framework for Graph-based Semi-supervised Learning

We develop a generalized optimization framework for graph-based 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 th...

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Bibliographic Details
Published inSociety for Industrial and Applied Mathematics. Proceedings of the SIAM International Conference on Data Mining p. 966
Main Authors Avrachenkov, Konstantin, Mishenin, Alexey, Gonçalves, Paulo, Sokol, Marina
Format Conference Proceeding
LanguageEnglish
Published Philadelphia Society for Industrial and Applied Mathematics 01.01.2012
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Summary:We develop a generalized optimization framework for graph-based 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 PageRank based method is robust with respect to the choice of the regularization parameter and the labelled data. We illustrate our theoretical results with two realistic datasets, characterizing different challenges: Les Miserables characters social network and Wikipedia hyper-link graph. The graph-based semi-supervised learning classifies the Wikipedia articles with very good precision and perfect recall employing only the information about the hyper-text links. [PUBLICATION ABSTRACT]