@CONFERENCE\{IMM2010-05921,
    author       = "C. Walder",
    title        = "Dirichlet Mixtures of Graph Diffusions for Semi Supervised Learning",
    year         = "2010",
    keywords     = "semi supervised learning, graph Laplacian, dirichlet process",
    booktitle    = "{IEEE} International Workshop on {MACHINE} {LEARNING} {FOR} {SIGNAL} {PROCESSING}",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5921-full.html",
    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 bench-mark data demonstrate that while computationally expensive our approach can provide significantly improved predictions in comparison with previous approaches."
}