Sampling Techniques for Kernel Methods

Achlioptas, D; McSherry, F; Schölkopf, B

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Sampling Techniques for Kernel Methods

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https://papers.nips.cc/paper/2072-sampling-techniques-for-kernel-methods.pdf
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Achlioptas, D., McSherry, F., & Schölkopf, B. (2002). Sampling Techniques for Kernel Methods. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 335-342). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DF14-2

Abstract

We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations.