Stable and Efficient Gaussian Process Calculations
Shared by Ashok Srivastava, updated on Sep 22, 2010
Summary
- Author(s) :
- L. Foster, A, A. Waagen, N. Aijaz, M. Hurley, A. Luis, J. Rinsky, C. Satyavolu, Michael Way, P. Gazis, Ashok Srivastava
- Abstract
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.
- Publication Name
- journal of Machine Learning Research
- Publication Location
- Accepted Dec 2008
- Year Published
- 2009
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