Discriminative Features for Identifying and Interpreting Outliers

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Provided by: Aarhus University
Topic: Big Data
Format: PDF
The authors consider the problem of outlier detection and interpretation. While most existing studies focus on the first problem, they simultaneously address the equally important challenge of outlier interpretation. They propose an algorithm that uncovers outliers in subspaces of reduced dimensionality in which they are well discriminated from regular objects while at the same time retaining the natural local structure of the original data to ensure the quality of outlier explanation. Their algorithm takes a mathematically appealing approach from the spectral graph embedding theory and they show that it achieves the globally optimal solution for the objective of subspace learning.
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