# Mercer Large-Scale Kernel Machines from Ridge Function Perspective

arXiv: https://arxiv.org/abs/2307.11925 · HTML: https://mlsanity.com/paper/2307.11925/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation](https://mlsanity.com/paper/2105.14368/) (2021)
- [Estimation of a regression function on a manifold by fully connected deep neural networks](https://mlsanity.com/paper/2107.09532/) (2021)

## Abstract (arXiv)

To present Mercer large-scale kernel machines from a ridge function perspective, we recall the results by Lin and Pinkus from it Fundamentality of ridge functions. We consider the main result of the recent paper by Rachimi and Recht, 2008, it Random features for large-scale kernel machines from the Approximation Theory point of view. We study which kernels could be approximated by a sum of products of cosine functions with arguments depending on x and y and present the obstacles of such an approach. The results of this article are applied to Image Processing by procedure "one-vs-rest".