Abstract:
Data anomaly detection has many real-world applications. Outlier detection algorithms are abundant, but most have hidden assumptions and restrictions.
This paper proposes a novel, yet effective outlier learning algorithm that decomposes the full attributes space into different subspaces and rotates the 3D-vectors, representing the data points per 3D-subspace, about the geometric median using Rodrigues rotation formula to calculate the overall outlying score.
The parameter-free, distribution-agnostic approach is simple to implement. Six popular outlier detection algorithms from different categories are tested on synthetic and real-world datasets. Precision @s, average precision, rank power, AUC ROC, and time complexity are compared. The proposed method performs well.
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