Computational study demonstrates effective anomaly identification in high-dimensional datasets using projection pursuit, highlighting a flexible alternative to hyperparameter-heavy methods.
new anomaly detection method called kernel outlier detection (KOD) is proposed.It is designed to address challenges of outlier detection in high-dimensionalsettings. The aim is to overcome limitations of existing methods, such as dependenceon 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 anew way to combine results of different direction types. This provides a flexibleand lightweight approach for outlier detection. Our empirical evaluations illustratethe effectiveness of KOD on three small datasets with challenging structures,and on four large benchmark datasets.
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Dağıdır et al. (2025) studied this question.
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