Dirichlet Mixtures of Graph Diffusions for Semi Supervised Learning

Christian Walder

AbstractGraph representations of data have emerged as powerful tools in the classification of partially labelled data. We give a new algorithm for graph based semi supervised learning which is based on a probabilistic model of the process which assigns labels to vertices. The main novelty is a non parametric mixture of graph diffusions, which we combine with a Markov random field potential. Markov chain Monte Carlo is used for the inference, which we demonstrate to be significantly better in terms of predictive power than the maximum a posteriori estimate. Experiments on bench-mark data demonstrate that while computationally expensive our approach can provide significantly improved predictions in comparison with previous approaches.
Keywordssemi supervised learning, graph Laplacian, dirichlet process
TypeConference paper [With referee]
ConferenceIEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING
Year2010
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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