论文标题
使用FastMUOD指数的多元功能异常值检测
Multivariate Functional Outlier Detection using the FastMUOD Indices
论文作者
论文摘要
我们介绍了快速大规模无监督的离群检测（FastMUOD）指数的定义和属性，该指标用于功能数据中的离群检测（OD）。 FastMuod通过计算每条曲线，一个振幅，幅度和形状索引来检测异常值，旨在针对相应的异常值类型。然后提出了一些将FastMuod适应多元功能数据中离群值检测的方法。其中包括在多元数据的组件上应用FastMuod并使用随机投影。此外，这些技术在各种模拟和真实的多元功能数据集上进行了测试。与多元功能OD中的最新技术相比，随机投影的使用显示出最有效的结果，并且在某些情况下改善了OD性能。
We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects outliers by computing, for each curve, an amplitude, magnitude and shape index meant to target the corresponding types of outliers. Some methods adapting FastMUOD to outlier detection in multivariate functional data are then proposed. These include applying FastMUOD on the components of the multivariate data and using random projections. Moreover, these techniques are tested on various simulated and real multivariate functional datasets. Compared with the state of the art in multivariate functional OD, the use of random projections showed the most effective results with similar, and in some cases improved, OD performance.
