Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior

Gaël Letarte; Emilie Morvant; Pascal Germain

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arXiv · 1810.12683

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior

Gaël Letarte·Emilie Morvant·Pascal Germain

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Abstract

We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF) method through the lens of the PAC-Bayesian theory. While the primary goal of RFF is to approximate a kernel, we look at the Fourier transform as a prior distribution over trigonometric hypotheses. It naturally suggests learning a posterior on these hypotheses. We derive generalization bounds that are optimized by learning a pseudo-posterior obtained from a closed-form expression. Based on this study, we consider two learning strategies: The first one finds a compact landmarks-based representation of the data where each landmark is given by a distribution-tailored similarity measure, while the second one provides a PAC-Bayesian justification to the kernel alignment method of Sinha and Duchi (2016).

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BibTeX RIS

Gaël Letarte, Emilie Morvant, Pascal Germain. 2018-10-30. Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior. https://arxiv.org/abs/1810.12683

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