Dirichlet Mixtures of Graph Diffusions for Semi Supervised Learning |
Christian Walder
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| 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. |
| 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 |