Journal of Data Science,
Statistics, and Visualisation

Kernel outlier detection

Authors

DOI:

https://doi.org/10.52933/jdssv.v5i8.152

Keywords:

Anomaly detection, outlyingness, kernel transformation, projection depth

Abstract

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.

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Published

2025-06-27

How to Cite

Dagidir, C. H., Hubert, M., & Rousseeuw, P. J. (2025). Kernel outlier detection. Journal of Data Science, Statistics, and Visualisation, 5(8). https://doi.org/10.52933/jdssv.v5i8.152

Issue

Section

Special Issue on DSSV 2025
Journal of Data Science,
Statistics, and Visualisation
Pages