# Efficient Nonlinear RX Anomaly Detectors

arXiv: https://arxiv.org/abs/2012.05799 · HTML: https://mlsanity.com/paper/2012.05799/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Anomaly and Change Detection with Multivariate Gaussianization](https://mlsanity.com/paper/2204.05699/) (2022)
- [Graph Embedding via High Dimensional Model Representation for Hyperspectral Images](https://mlsanity.com/paper/2111.14680/) (2021)
- [Investigation of unsupervised and supervised hyperspectral anomaly detection](https://mlsanity.com/paper/2408.07114/) (2024)
- [Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study](https://mlsanity.com/paper/2507.05730/) (2025)

## Abstract (arXiv)

Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either em data-independent random Fourier features or em data-dependent basis with the Nystr"om approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks to their implicit regularization effect. Last but not least, the Nystr"om approach has an improved power of detection.