Nystr\"om Kernel Mean Embeddings. (arXiv:2201.13055v2 [stat.ML] UPDATED)

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

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 …

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