State Space representation of non-stationary Gaussian Processes
This work addresses a domain-specific need in machine learning for scalable Gaussian process inference, but it is incremental as it builds on existing state space methods.
The paper tackles the problem of efficiently representing non-stationary Gaussian process kernels using state space models, achieving O(n) computational complexity to enable scalability for Big Data applications.
The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest. The main reason is that it allows to compute GPs based inferences in O(n), where $n$ is the number of observations. This implementation makes GPs suitable for Big Data. For this reason, it is important to provide a SS representation of the most important kernels used in machine learning. The aim of this paper is to show how to exploit the transient behaviour of SS models to map non-stationary kernels to SS models.