Detection and prognostics on low-dimensional systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Greener aviation with virtual sensors: a case study
Data Mining and Knowledge Discovery
Implementing a gaussian process learning algorithm in mixed parallel environment
Proceedings of the second workshop on Scalable algorithms for large-scale systems
Activity representation with motion hierarchies
International Journal of Computer Vision
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The use of Gaussian processes can be an effective approach to prediction in a supervised learning environment. For large data sets, the standard Gaussian process approach requires solving very large systems of linear equations and approximations are required for the calculations to be practical. We will focus on the subset of regressors approximation technique. We will demonstrate that there can be numerical instabilities in a well known implementation of the technique. We discuss alternate implementations that have better numerical stability properties and can lead to better predictions. Our results will be illustrated by looking at an application involving prediction of galaxy redshift from broadband spectrum data.