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A Novel Outlier Detection Method for Multivariate Data.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Sep2022, Vol. 34 Issue 9, p4052-4062, 11p
- Publication Year :
- 2022
-
Abstract
- Detecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions. This paper proposes a novel, yet effective outlier learning algorithm that is based on decomposing the full attributes space into different combinations of subspaces, in which the 3D-vectors, representing the data points per 3D-subspace, are rotated about the geometric median, using Rodrigues rotation formula, to construct the overall outlying score. The proposed approach is parameter-free, requires no distribution assumptions and easy to implement. Extensive experimental study and comparison are conducted on both synthetic and real-world datasets with six popular outlier detection algorithms, each from different category. The comparison is evaluated based on the precision @s, average precision, rank power, AUC ROC and time complexity metrics. The results show that the performance of the proposed method is competitive and promising. [ABSTRACT FROM AUTHOR]
- Subjects :
- OUTLIER detection
MACHINE learning
EUCLIDEAN distance
COST functions
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 34
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 158405959
- Full Text :
- https://doi.org/10.1109/TKDE.2020.3036524