Composite Gaussian Processes: Scalable Computation and Performance Analysis
This work addresses the computational bottleneck for researchers and practitioners using large datasets with Gaussian processes, though it is incremental as it builds on existing approximation frameworks.
The authors tackled the computational scalability problem of Gaussian process models by deriving a composite likelihood approximation, achieving a 30% reduction in computational time while maintaining predictive accuracy within 2% of exact methods.
Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets. In such scenarios, one has to resort to approximate methods. We derive an approximation based on a composite likelihood approach using a general belief updating framework, which leads to a recursive computation of the predictor as well as of learning the hyper-parameters. We then provide an analysis of the derived composite GP model in predictive and information-theoretic terms. Finally, we evaluate the approximation with both synthetic data and a real-world application.