Parsimonious Online Learning with Kernels via sparse projections in function space

@article{Koppel2017ParsimoniousOL,
title={Parsimonious Online Learning with Kernels via sparse projections in function space},
author={Alec Koppel and Garrett Warnell and Ethan Stump and Alejandro Ribeiro},
journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2017},
pages={4671-4675}
}
• Published 13 December 2016
• Computer Science
• 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
We consider stochastic nonparametric regression problems in a reproducing kernel Hilbert space (RKHS), an extension of expected risk minimization to nonlinear function estimation. Popular perception is that kernel methods are inapplicable to online settings, since the generalization of stochastic methods to kernelized function spaces require memory storage that is cubic in the iteration index (“the curse of kernelization”). We alleviate this intractability in two ways: (1) we consider the use…

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