Dirichlet Mixtures of Graph Diffusions for Semi Supervised Learning 
Christian Walder

Abstract  Graph 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 benchmark data demonstrate that while computationally expensive our approach can provide significantly improved predictions in comparison with previous approaches. 
Keywords  semi supervised learning, graph Laplacian, dirichlet process 
Type  Conference paper [With referee] 
Conference  IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING 
Year  2010 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Intelligent Signal Processing 