Archipelago: nonparametric Bayesian semi-supervised learning

Authors:
Ryan Prescott Adams;Zoubin Ghahramani
Affiliations:
University of Cambridge, Cambridge, UK;University of Cambridge, Cambridge, UK and Carnegie Mellon University, Pittsburgh, PA
Venue:
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Year:
2009

Citing 5
Cited 1

Bayesian Classification With Gaussian Processes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Bayesian multinomial probit regression with Gaussian process priors

Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression

The Journal of Machine Learning Research
Semi-supervised Gaussian process classifiers

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence

Discriminative deep belief networks for visual data classification

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.