Web: http://arxiv.org/abs/2201.13055

June 16, 2022, 1:12 a.m. | Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi, Lorenzo Rosasco

stat.ML updates on arXiv.org arxiv.org

Kernel mean embeddings are a powerful tool to represent probability
distributions over arbitrary spaces as single points in a Hilbert space. Yet,
the cost of computing and storing such embeddings prohibits their direct use in
large-scale settings. We propose an efficient approximation procedure based on
the Nystr\"om method, which exploits a small random subset of the dataset. Our
main result is an upper bound on the approximation error of this procedure. It
yields sufficient conditions on the subsample size to …

arxiv kernel mean ml

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