Fast Sparse Gaussian Process Methods: The Informative Vector Machine

  title={Fast Sparse Gaussian Process Methods: The Informative Vector Machine},
  author={Neil D. Lawrence and Matthias W. Seeger and Ralf Herbrich},
We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on informationtheoretic principles, previously suggested for active learning. Our goal is not only to learn d–sparse predictors (which can be evaluated in O(d) rather than O(n), d ≪ n, n the number of training points), but also to perform training under strong restrictions on time and memory requirements. The scaling of our method is at most O(n · d), and in large real-world… CONTINUE READING
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