# Feature space approximation for kernel-based supervised learning

arXiv: https://arxiv.org/abs/2011.12651 · HTML: https://mlsanity.com/paper/2011.12651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Approximate Bayesian Computation Based on Maxima Weighted Isolation Kernel Mapping](https://mlsanity.com/paper/2201.12745/) (2022)

## Abstract (arXiv)

We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. Furthermore, the method can be regarded as a regularization technique, which improves the generalizability of learned target functions. We demonstrate significant improvements in comparison to the computation of data-driven predictions involving the full training data set. The method is applied to classification and regression problems from different application areas such as image recognition, system identification, and oceanographic time series analysis.