Work

Outlier detection of multivariate data via the maximization of the cumulant generating function

Year: 2024

Type: article

Abstract: In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n -circle as a concave minimization problem. Then, we show that the CGF maximization approach can be interpreted as an extension of the standard principal component technique. Therefore, for validation and testing, we provide a thorough comparison of our methodology with two other projection-based approaches both on artificial and real-world financial data. Finally, we apply our method as an early detector for financial crises.

Source: Journal of Computational and Applied Mathematics

DOI: 10.1016/j.cam.2024.116457

Authors: Francesco Cesarone, Rosella Giacometti, Jacopo Maria Ricci

Institutions: Roma Tre University, University of Bergamo

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,160

Funder: Ministero dell’Istruzione, dell’Università e della Ricerca