A Novel Outlier Detection Method for Multivariate Data

Almardeny, Yahya and Boujnah, Noureddine and Cleary, Frances (2020) A Novel Outlier Detection Method for Multivariate Data. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347 (In Press)

Full text not available from this repository. (Request a copy)


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.

Item Type: Article
Additional Information: Publisher Copyright: IEEE
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1710
Departments or Groups:
Depositing User: Admin SSL
Date Deposited: 17 Oct 2022 16:06
Last Modified: 16 Aug 2023 23:01
URI: http://repository-testing.wit.ie/id/eprint/3546

Actions (login required)

View Item View Item