Anomaly Detection Based on Convex Analysis: A Survey

Frontiers in Physics

Mengsi Cai¹  Xiao Ouyang²  Tong Wang³  Xin Lu⁴  Ziqiang Cao⁵  Xu Tan⁶  Tie Cai⁶ 

[1] College of Economy and Management, Changsha University, Changsha, China;College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, China;College of Systems Engineering, National University of Defense Technology, Changsha, China;Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden;Power China Zhongnan Engineering Corporation Limited, Changsha, China;School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, China;

关键词: anomaly detection;    convex analysis;    density estimation;    matrix factorization;    machine learning;   

DOI  :  10.3389/fphy.2022.873848

来源: DOAJ

【 摘 要 】

As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. Convex analysis (CA) is one of the fundamental methods used in anomaly detection, which contributes to the robust approximation of algebra and geometry, efficient computation to a unique global solution, and mathematical optimization for modeling. Despite the essential role and evergrowing research in CA-based anomaly detection algorithms, little work has realized a comprehensive survey of it. To fill this gap, we summarize the CA techniques used in anomaly detection and classify them into four categories of density estimation methods, matrix factorization methods, machine learning methods, and the others. The theoretical background, sub-categories of methods, typical applications as well as strengths and limitations for each category are introduced. This paper sheds light on a succinct and structured framework and provides researchers with new insights into both anomaly detection and CA. With the remarkable progress made in the techniques of big data and machine learning, CA-based anomaly detection holds great promise for more expeditious, accurate and intelligent detection capacities.

【 授权许可】

Unknown