Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Gaussian processes (GP) are Bayesian non- parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This paper presents two parallel GP regression methods tha...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Association for Uncertainty in Artificial Intelligence Press,
2014-05-16T14:13:51Z.
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| Subjects: | |
| Online Access: | Get fulltext |