CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD
Andreas Lohrer, Daniyal Kazempour, Maximilian H\"unem\"order, Peer, Kr\"oger

TL;DR
CoMadOut is a new robust outlier detection algorithm based on comedian PCA, designed to be resilient to outliers and effective in identifying them, outperforming or matching existing methods in key metrics.
Contribution
The paper introduces CoMadOut, a robust PCA-based outlier detection method that effectively handles outliers while maintaining high detection performance.
Findings
Competitive performance in AP, AUPRC, AUROC metrics
Robustness against outliers demonstrated in experiments
Effective distribution-based outlier scoring
Abstract
Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Adversarial Robustness in Machine Learning · Water Systems and Optimization
