PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Archipelago: Nonparametric Bayesian Semi-Supervised Learning
Ryan Adams and Zoubin Ghahramani
Proceedings of the 26th Annual International Conference on Machine Learning pp. 1-8, 2009.

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.

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EPrint Type:Article
Additional Information:Honourable mention for best overall paper at ICML 2009
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5425
Deposited By:Ryan Adams
Deposited On:08 March 2010