Parallel Gaussian process regression for big data: Low-rank representation meets markov approximation
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the data size. To improve its scalability, this paper presents a low-rank-cum-Markov approximation (LMA) of the GP model that is novel in leveraging the dual computational advantages stemming from complement...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computing Machinery,
2018-06-12T17:40:35Z.
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| Subjects: | |
| Online Access: | Get fulltext |