Title
Malleable Kernel Interpolation for Scalable Structured Gaussian Process
Author
Ban, Hanyuan (TU Delft Electrical Engineering, Mathematics and Computer Science)
Contributor
Rajan, R.T. (mentor)
Fioranelli, F. (graduation committee)
Giovanardi, Bianca (graduation committee)
Degree granting institution
Delft University of Technology
Programme
Electrical Engineering
Date
2023-11-07
Abstract
Gaussian process regression (GPR), a potent non-parametric data modeling tool, has gained attention but is hindered by its high com- putational load. State-of-the-art low-rank approximations like struc- tured kernel interpolation (SKI)-based methods offer efficiency, yet lack a strategy for determining the number of grid points, a pivotal factor impacting accuracy and efficiency. In this thesis, we tackle this challenge.
We explore existing low-rank approximations that facilitates the computation, dissecting their strengths and limitations, particularly SKI-based methods. Subsequently, we introduce a novel approxima- tion framework, MKISSGP, which dynamically adjusts grid points us- ing a new hyperparameter of the model: density, according to changes in the kernel hyperparameters in each training iteration.
MKISSGP exhibited consistent error levels in the reconstruction of the kernel matrix, irrespective of changes in hyperparameters. This robust performance forms the bedrock for achieving accurate approx- imations of kernel matrix-related terms. When employing our rec- ommended density value (i.e., 2.7), MKISSGP achieved a comparable level of precision to that of precise GPR, while requiring only 52% of the time compared to the current state-of-the-art method.
Subject
Gaussian process regression
Low-rank approximation
Structured kernel interpolation
Grid points
Density
To reference this document use:
http://resolver.tudelft.nl/uuid:323f5836-e73a-4bab-83bb-045803f25ffb
Embargo date
2024-05-07
Part of collection
Student theses
Document type
master thesis
Rights
© 2023 Hanyuan Ban