# Kernel Outlier Detection

arXiv: https://arxiv.org/abs/2506.22994 · HTML: https://mlsanity.com/paper/2506.22994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Depth and depth-based classification with R-package ddalpha](https://mlsanity.com/paper/1608.04109/) (2016)
- [Class maps for visualizing classification results](https://mlsanity.com/paper/2007.14495/) (2020)
- [A Measure of Directional Outlyingness with Applications to Image Data and Video](https://mlsanity.com/paper/1608.05012/) (2016)
- [Outlier detection in non-elliptical data by kernel MRCD](https://mlsanity.com/paper/2008.02046/) (2020)
- [Kernel Random Projection Depth for Outlier Detection](https://mlsanity.com/paper/2306.07056/) (2023)

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

- [Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection](https://mlsanity.com/paper/2510.24043/) (2025)

## Abstract (arXiv)

A new anomaly detection method called kernel outlier detection (KOD) is proposed. It is designed to address challenges of outlier detection in high-dimensional settings. The aim is to overcome limitations of existing methods, such as dependence on distributional assumptions or on hyperparameters that are hard to tune. KOD starts with a kernel transformation, followed by a projection pursuit approach. Its novelties include a new ensemble of directions to search over, and a new way to combine results of different direction types. This provides a flexible and lightweight approach for outlier detection. Our empirical evaluations illustrate the effectiveness of KOD on three small datasets with challenging structures, and on four large benchmark datasets.