Linear Models for Outlier Detection
The attributes in real data are usually highly correlated. Such dependencies provide the ability to predict attributes from one another. The notions of prediction and anomaly detection are intimately related. Outliers are, after all, values that deviate from expected (or predicted) values on the basis of a particular model. Linear models focus on the use of interattribute dependencies to achieve this goal. In the classical statistics literature, this process is referred to as regression modeling.
KeywordsPrincipal Component Analysis Hide Layer Outlier Detection Kernel Principal Component Analysis Latent Semantic Indexing
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