Robust Multi-Class Gaussian process classification
(2011) Neural Information Processing Systems conference (NIPS′11) — Location: Granada, Spain (13.December.2011)
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- Authors
- Abstract
- Multi-class Gaussian Process Classifiers (MGPCs) are often affected by overfitting problems when labeling errors occur far from the decision boundaries. To prevent this, we investigate a robust MGPC (RMGPC) which considers labeling errors independently of their distance to the decision boundaries. Expectation propagation is used for approximate inference. Experiments with several datasets in which noise is injected in the labels illustrate the benefits of RMGPC. This method performs better than other Gaussian process alternatives based on considering latent Gaussian noise or heavy-tailed processes. When no noise is injected in the labels, RMGPC still performs equal or better than the other methods. Finally, we show how RMGPC can be used for successfully identifying data instances which are difficult to classify correctly in practice.
- Affiliations
University of Cambridge Department of Engineering
Citations
Hernandez-Lobato, D., Hernandez-Lobato, J. M., & Dupont, P. (2011). Robust Multi-Class Gaussian process classification. Neural Information Processing Systems conference (NIPS′11), Granada, Spain. https://hdl.handle.net/2078.5/253961