Deep unsupervised anomaly detection

• Main Authors: LI, Tangqing, WANG, Zheng, LIU, Siying, LIN, Wen-yan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Computer vision; clustering algorithms; benchmark testing; reliability; anomaly detection; Computer Sciences; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/6111; https://ink.library.smu.edu.sg/context/sis_research/article/7114/viewcontent/0184.pdf
• Institution: Singapore Management University

This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data....