异常检测
离群值
期望最大化算法
计算机科学
最大化
概率逻辑
混合模型
人工智能
高斯分布
模式识别(心理学)
数据挖掘
算法
统计
数学
最大似然
数学优化
物理
量子力学
作者
Zhikun Zhang,Yongjian Duan,Xiangjun Wang,Mingyuan Zhang
出处
期刊:Chaos
[American Institute of Physics]
日期:2025-04-01
卷期号:35 (4)
摘要
This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection (EMOD) algorithm, which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems. The EMOD is based on a two-state Gaussian mixture model and demonstrates strong performance in probability anomaly detection working on real-time raw data rather than using special prior distribution information. We confirm this using the synthetic data from two numerical cases. For the real-world data, we have detected the short-circuit pattern of the circuit system using EMOD by the current and voltage output of a three-phase inverter. The EMOD also found an abnormal period due to COVID-19 in the insured unemployment data of 53 regions in the United States from 2000 to 2024. The application of EMOD to these two real-life datasets demonstrated the effectiveness and accuracy of our algorithm.
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