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- W4288412760 abstract "Random features provide a practical framework for large-scale kernel approximation and supervised learning. It has been shown that data-dependent sampling of random features using leverage scores can significantly reduce the number of features required to achieve optimal learning bounds. Leverage scores introduce an optimized distribution for features based on an infinite-dimensional integral operator (depending on input distribution), which is impractical to sample from. Focusing on empirical leverage scores in this paper, we establish an out-of-sample performance bound, revealing an interesting trade-off between the approximated kernel and the eigenvalue decay of another kernel in the domain of random features defined based on data distribution. Our experiments verify that the empirical algorithm consistently outperforms vanilla Monte Carlo sampling, and with a minor modification the method is even competitive to supervised data-dependent kernel learning, without using the output (label) information." @default.
- W4288412760 created "2022-07-29" @default.
- W4288412760 creator A5040922600 @default.
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- W4288412760 date "2019-03-19" @default.
- W4288412760 modified "2023-10-14" @default.
- W4288412760 title "On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees" @default.
- W4288412760 doi "https://doi.org/10.48550/arxiv.1903.08329" @default.
- W4288412760 hasPublicationYear "2019" @default.
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