Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a model of the data’s probability density can assist in identifying clusters. Nonparametric Bayesian methods, while ideal in theory due to their principled motivations, have been difficult to apply to SSL in practice. We present a nonparametric Bayesian method that uses Gaussian processes for the generative model, avoiding many of the problems associated with Dirichlet process mixture models. Our model is fully generative and we take advantage of recent advances in Markov chain Monte Carlo algorithms to provide a practical inference method. Our method compares favorably to competing approaches on synthetic and real-world multi-class data.
@conference{adams2009archipelago,
year = {2009},
author = {Adams, Ryan P. and Ghahramani, Zoubin},
title = {Archipelago: Nonparametric {B}ayesian Semi-Supervised Learning},
booktitle = {Proceedings of the 26th International Conference on Machine Learning (ICML)},
keywords = {Gaussian processes, Bayesian nonparametrics, semi-supervised learning, Markov chain Monte Carlo, ICML}
}