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Gaussian processes are a powerful non-parametic tool for Bayesian inference but limited by their cubic scaling problem. This paper aims to develop the single-task and multitask sparse Gaussian processes for both regression and classification problems. Firstly, we apply a manifold-preserving graph reduction algorithm to construct the single-task sparse Gaussian processes from a sparse graph perspective...
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