Summary of Fast Evaluation Of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel Derivatives, by Theresa Wagner et al.
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel Derivatives
by Theresa Wagner, Franziska Nestler, Martin Stoll
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a technique to efficiently approximate the matrix-vector product for kernel-based learning in high-dimensional feature spaces. The approach is based on the non-equispaced fast Fourier transform (NFFT) and includes rigorous error analysis. This method is suitable for approximating the matrix that arises when differentiating the kernel with respect to its hyperparameters, a common problem in Gaussian process training. The paper also provides an illustration of the additive kernel scheme’s performance on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a way to quickly calculate large and complex matrices used in kernel-based learning. This is especially important when working with data that has many features or dimensions. They tested their method and showed it can be used to improve the speed and accuracy of algorithms like Gaussian processes. The code for this method is available online. |
