Abstract
Gaussian process classifiers (GPCs) are a fully statistical model for kernel classification. We present a form of GPC which is robust to labeling errors in the data set. This model allows label noise not only near the class boundaries, but also far from the class boundaries which can result from mistakes in labelling or gross errors in measuring the input features. We derive an outlier robust algorithm for training this model which alternates iterations based on the EP approximation and hyperparameter updates until convergence. We show the usefulness of the proposed algorithm with model selection method through simulation results.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Williams, C.K.I., Barber, D.: Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis Machine Intelligence 20, 1342–1351 (1998)
Gibbs, M., MacKay, D.J.C.: Variational Gaussian process classifiers. IEEE Transcations on Neural Networks 11(6), 1458 (2000)
Neal, R.: Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical Report CRG–TR–97–2, Dept. of Computer Science, University of Toronto (1997)
Opper, M., Winther, O.: Gaussian processes for classification: Mean field algorithms. Neural Computation 12, 2655–2684 (2000)
Minka, T.: A family of algorithms for approximate Bayesian inference. PhD thesis, MIT (2001)
Williams, C.K.I., Rasmussen, C.E.: Gaussian processes for regression. In: NIPS, vol. 8. MIT Press, Cambridge (1995)
Seeger, M., Lawrence, N., Herbrich, R.: Sparse representation for Gaussian process models. In: NIPS, vol. 15 (2002)
Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Machine Learning 37, 183–233 (1999)
Neal, R., Hinton, G.: A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan, M.I. (ed.) Learning in Graphical Models. Kluwer, Dordrecht (1998)
MacKay, D.J.C.: Bayesian interpolation. Neural Computation 4(3), 415–447 (1992)
Kim, H.C., Ghahramani, Z.: The EM-EP algorithm for Gaussian process classification. In: Proceedings of the Workshop on Probabilistic Graphical Models for Classification (ECML) (2003)
Kim, H.C., Ghahramani, Z.: Bayesian Gaussian process classification with the EM-EP algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 1948–1959 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, HC., Ghahramani, Z. (2008). Outlier Robust Gaussian Process Classification. In: da Vitoria Lobo, N., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2008. Lecture Notes in Computer Science, vol 5342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89689-0_93
Download citation
DOI: https://doi.org/10.1007/978-3-540-89689-0_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89688-3
Online ISBN: 978-3-540-89689-0
eBook Packages: Computer ScienceComputer Science (R0)