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We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression.
- The algorithm estimates an inducing set and the hyperparameters using a single objective, either the marginal likelihood or a variational free energy.
- The space and time complexity are linear in training set size, and the algorithm can be applied to large regression problems on discrete or continuous domains.
- Empirical evaluation shows state-of-art performance in discrete cases and competitive results in the continuous case.
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