Streaming Sparse Gaussian Process Approximations
This provides a solution for deploying Gaussian process models in real-time applications with streaming data, though it appears incremental as it builds on sparse pseudo-point approximations.
The paper tackles the problem of handling streaming data with Gaussian process models by developing a principled framework that updates both posterior distributions and hyperparameters online, addressing issues like catastrophic forgetting and slow updating in existing approaches.
Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the hyperparameter estimates are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing methods for learning hyperparameters and optimising pseudo-input locations. The proposed framework is assessed using synthetic and real-world datasets.