Malleable Kernel Interpolation for Scalable Structured Gaussian Process

Hanyuan Ban, Ellen H.J. Riemens, Raj Thilak Rajan

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Gaussian process regression (GPR), is a powerful non-parametric approach for data modeling, which has garnered considerable interest in the past decade, however its widespread application is impeded by the significant computational burden for larger datasets. The computational complexity for both inference and hyperparameter learning in GPs lead to O(N3) for N training points. The current state-of-the-art approximations, such as structured kernel interpolation (SKI)-based methods e.g., Kernel Interpolation for Scalable Structured Gaussian Process (KISSGP), have emerged to mitigate this challenge by providing a scalable inducing point alternatives. However, the choice of the optimal number of grid points, which influences the accuracy and efficiency of the model, typically remains fixed and is chosen arbitrarily. In this work, we introduce a novel approximation framework, Malleable KISSGP (MKISSGP), which dynamically adjusts grid points using a new hyperparameter of the model called density, which adapts to the changes in the kernel hyperparameters in each training iteration. In comparison with the state-of-the-art KISSGP and irrespective of changes in hyperparameters, our proposed MKISSGP algorithm exhibits consistent error levels in the reconstruction of the kernel matrix, and offers reduced computational complexity. We present extensive simulations to validate the improved performance of the proposed MKISSGP, and give directions for future research.

Original languageEnglish
Title of host publication32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages997-1001
Number of pages5
ISBN (Electronic)9789464593617
DOIs
Publication statusPublished - 2024
Event32nd European Signal Processing Conference, EUSIPCO 2024 - Lyon, France
Duration: 26 Aug 202430 Aug 2024
https://eusipcolyon.sciencesconf.org/

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference32nd European Signal Processing Conference, EUSIPCO 2024
Abbreviated titleEUSIPCO 2024
Country/TerritoryFrance
CityLyon
Period26/08/2430/08/24
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Gaussian process regression
  • KISSGP
  • Low-rank approximation
  • Structured kernel interpolation

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