Nonparametric Bayesian label prediction on a graph
Computation
2017-06-16 v2 Machine Learning
Abstract
An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described. A hierarchical Bayesian approach with a randomly scaled Gaussian prior is considered. The prior uses the graph Laplacian to take into account the underlying geometry of the graph. A method based on a theoretically optimal prior and a more flexible variant using partial conjugacy are proposed. Two simulated data examples and two examples using real data are used in order to illustrate the proposed methods.
Cite
@article{arxiv.1612.01930,
title = {Nonparametric Bayesian label prediction on a graph},
author = {Jarno Hartog and Harry van Zanten},
journal= {arXiv preprint arXiv:1612.01930},
year = {2017}
}