Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase

Jan Graßhoff, Alexandra Jankowski, Philipp Rostalski

Abstract

The application of Gaussian processes (GPs) to large data sets is limited due to heavy memory and computational requirements. A variety of methods has been proposed to enable scalability, one of which is to exploit structure in the kernel matrix. Previous methods, however, cannot easily deal with mixtures of non-stationary processes. This paper investigates an efficient GP framework, that extends structured kernel interpolation methods to GPs with a non-stationary phase. We particularly treat the separation of nonstationary sources, which is a problem that commonly arises e.g. in spatio-temporal biomedical datasets. Our approach employs multiple sets of non-equidistant inducing points to account for the non-stationarity and retrieve Toeplitz and Kronecker structure in the kernel matrix allowing for efficient inference and kernel learning. Our approach is demonstrated on numerical examples and large spatio-temporal biomedical problems.
OriginalspracheEnglisch
Seitenumfang12
PublikationsstatusVeröffentlicht - 2020
VeranstaltungProceedings of the 37th International Conference on Machine Learning - Virtual Event
Dauer: 13.07.202018.07.2020
https://dblp.org/rec/conf/icml/2020.html

Tagung, Konferenz, Kongress

Tagung, Konferenz, KongressProceedings of the 37th International Conference on Machine Learning
KurztitelICML 2020
Zeitraum13.07.2018.07.20
Internetadresse

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