Journal
PATTERN RECOGNITION LETTERS
Volume 45, Issue -, Pages 85-91Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.patrec.2014.03.004
Keywords
Gaussian processes; Parameter learning; Kalman filtering
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Two approaches for on-line Gaussian process regression with low computational and memory demands are proposed. The first approach assumes known hyperparameters and performs regression on a set of basis vectors that stores mean and covariance estimates of the latent function. The second approach additionally learns the hyperparameters on-line. For this purpose, techniques from nonlinear Gaussian state estimation are exploited. The proposed approaches are compared to state-of-the-art sparse Gaussian process algorithms. (C) 2014 Elsevier B.V. All rights reserved.
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