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Gaussian process variance reduction by location selection
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DOI：10.1016/j.patrec.2019.07.013.png)
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Abstract
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A key issue in Gaussian Process modeling is to decide on the locations where measurements are going to be taken. A good set of observations will provide a better model. Current state of the art selects such a set so as to minimize the posterior variance of the Gaussian process by exploiting submodularity. We propose two techniques, a Gradient Descent procedure and an heuristic algorithm to iteratively improve an initial set of observations so as to minimize the posterior variance directly. Results show the clear improvements that can be obtain under different settings. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Gaussian process
Variance reduction
Gradient descent
Sampling
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