Mind the nuisance: Gaussian process classification using privileged noise

Hernández-lobato, Daniel, Sharmanska, Viktoriia, Kersting, Kristian, Lampert, Christoph H and Quadrianto, Novi (2014) Mind the nuisance: Gaussian process classification using privileged noise. Published in: Proceedings of Advances in Neural Information Processing Systems 27 (NIPS 2014); Palais des Congrès de Montréal, Montréal Canada; 8 - 13 December 2014. 837-845. Neural Information Processing Systems Foundation

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Abstract

The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time. Here, we show that privileged information can naturally be treated as noise in the latent function of a Gaussian process classifier (GPC). That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC probit likelihood function. Extensive experiments on public datasets show that the proposed GPC method using privileged noise, called GPC+, improves over a standard GPC without privileged knowledge, and also over the current state-of-the-art SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep learning methods can be compressed as privileged information.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
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Depositing User: Novi Quadrianto
Date Deposited: 18 Jun 2015 20:15
Last Modified: 16 Jun 2017 10:36
URI: http://sro.sussex.ac.uk/id/eprint/54603

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