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New method improves random Fourier feature sampling for ML

By PulseAugur Editorial · [1 sources] ·

This research paper introduces an adaptive resampling method for random Fourier features, a technique used in machine learning for high-dimensional data. The proposed method aims to improve the sampling of Fourier frequencies, which has been a challenge in the field. The authors provide a theoretical proof of convergence for their data-adaptive approach, demonstrating its effectiveness in regression and classification tasks through numerical experiments. AI

IMPACT Introduces a novel theoretical and numerical approach to enhance machine learning algorithms for high-dimensional data analysis.

RANK_REASON The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method improves random Fourier feature sampling for ML

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The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xin Huang, Aku Kammonen, Anamika Pandey, Mattias Sandberg, Erik von Schwerin, Anders Szepessy, Ra\'ul Tempone ·

    Convergence for adaptive resampling of random Fourier features

    arXiv:2509.03151v2 Announce Type: replace-cross Abstract: The machine learning random Fourier feature method for data in high dimension is computationally and theoretically attractive since the optimization is based on a convex standard least squares problem and independent sampl…

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