图 书 馆
基于帕累托深度分析的多准则异常检测Multi-criteria Anomaly Detection using Pareto Depth Analysis |
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| 课程网址: | http://videolectures.net/machine_hero_anomaly_detection/ |
| 主讲教师: | Alfred O. Hero III |
| 开课单位: | 密歇根大学 |
| 开课时间: | 2013-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria by taking some linear combination of them. If the importance of the different criteria are not known in advance, the algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we introduce a novel non-parametric multi-criteria anomaly detection method using Pareto depth analysis (PDA). PDA uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach scales linearly in the number of criteria and is provably better than linear combinations of the criteria. |
| 关 键 词: | 异常行为; 数据集中; 标准计算 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：cwx |
| 阅读次数: | 91 |