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For a properly scaled continuous positive-definite shift-invariant kernel, drawing frequencies from the kernel's Fourier transform produces random sinusoidal features whose inner product is an unbiased estimator of the kernel; averaging D independent frequency draws lowers estimator variance. · CiteArk

Random Features for Large-Scale Kernel Machines

Not assessedNo independent reproduction scheduledMethodclaim-fourier-unbiased-map

For a properly scaled continuous positive-definite shift-invariant kernel, drawing frequencies from the kernel's Fourier transform produces random sinusoidal features whose inner product is an unbiased estimator of the kernel; averaging D independent frequency draws lowers estimator variance.

Source: paper:PDF pp. 2-4, Section 3, Eq. (2), Figure 1, and Algorithm 1

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Research Plan identity

Claim version: sha256:13c4f66acef33a78a5f937680f59bcee41afe9bd31e9d0cf1c15d990ae0b4dc8
Planned experiments: 0
Evidence objects: 0

Initial preparation has not resolved this question; retain it for subsequent investigation and coverage assessment.

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