TY - GEN
T1 - Archipelago
T2 - 26th International Conference On Machine Learning, ICML 2009
AU - Adams, Ryan Prescott
AU - Ghahramani, Zoubin
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=71149110678&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71149110678&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:71149110678
SN - 9781605585161
T3 - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
SP - 1
EP - 8
BT - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
Y2 - 14 June 2009 through 18 June 2009
ER -