Hilbert space methods for reduced-rank Gaussian process regression

This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in a compact subset of R d . On this approximate eigenbasis, the eigenvalu...

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Veröffentlicht in:	Statistics and computing Jg. 30; H. 2; S. 419 - 446
Hauptverfasser:	Solin, Arno, Särkkä, Simo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2020 Springer Nature B.V
Schlagworte:	Artificial Intelligence Basis functions Computer simulation Covariance Data points Density Eigenvalues Eigenvectors Empirical analysis Error analysis Gaussian process Hilbert space Learning Mathematics and Statistics Operators (mathematics) Probability and Statistics in Computer Science Regression analysis Series expansion Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs Truncation errors Laplace operator Eigenfunction expansion Gaussian process regression Reduced-rank approximation Pseudo-differential operator
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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