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Anomaly detection by robust statistics
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DOI：10.1002/widm.1236.png)
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Abstract
En 中文
Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential. A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. We present an overview of several robust methods and the resulting graphical outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data, such as estimating location and scatter, linear regression, principal component analysis, classification, clustering, and functional data analysis. Also the challenging new topic of cellwise outliers is introduced. (c) 2017 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals, Inc.
Keywords:
PRINCIPAL COMPONENT ANALYSIS
MULTIVARIATE LOCATION
MATLAB LIBRARY
LINEAR-MODEL
REGRESSION
ESTIMATORS
COVARIANCE
DISPERSION
ALGORITHM
SELECTION
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