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Cited 4 time in webofscience Cited 7 time in scopus
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PUMAD: PU Metric learning for anomaly detection

Title
PUMAD: PU Metric learning for anomaly detection
Authors
HYUNJUN JUDONGHA LEEJUNYOUNG HWANGJunghyun NamkungHwanjo Yu
Date Issued
Jun-2020
Publisher
ELSEVIER SCIENCE INC
Abstract
Anomaly detection task, which identifies abnormal patterns in data, has been widely applied to various domains. Most recent work on anomaly detection have focused on an accurate modeling of the normal data based on unsupervised methods. To get a satisfactory anomaly detection accuracy, they need pure normal data without abnormal data. This scenario requires many labels to get pure normal data. In many real-world scenarios, there exist abundant unlabeled data and a limited number of partially labeled anomalies. This paper proposes a novel anomaly detection method, PUMAD, which uses a Positive and Unlabeled (PU) learning approach to learn from abundant unlabeled data and a small number of partially labeled anomalies (i.e., positives). PUMAD successfully works on the anomaly detection scenario by exploiting deep metric learning with a hashing-based filtering method. Extensive experimental results on real-world benchmark datasets demonstrate that our approach based on PU learning is effective to detect anomalies. PUMAD achieves a much higher accuracy of up to 24% than state-of-the-art competitors. (C) 2020 Elsevier Inc. All rights reserved.
URI
http://oasis.postech.ac.kr/handle/2014.oak/103584
ISSN
0020-0255
Article Type
Article
Citation
INFORMATION SCIENCES, vol. 523, page. 167 - 183, 2020-06
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유환조YU, HWANJO
Dept of Computer Science & Enginrg
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