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2013.ECML PKDD.Local Outlier Detection with Interpretation

  • paper
  • main idea
  • contribution
  • method
    • Neighboring Set Selection
    • Anomaly Degree Computation
    • Outlier Interpretation
    • citation
  • experiment

paper

pdf

main idea

In LODI, we develop an approach that explores the quadratic entropy to adaptively select a set of neighboring instances, and a learning method to seek an optimal subspace in which an outlier is maximally separated from its neighbors.
We show that this learning task can be solved via the matrix eigen-decomposition and its solution contains essential information to reveal features that are most important to interpret the exceptional properties of outliers.

contribution

We propose a technique relying on the information theoretic measure of entropy to select an appropriate set of neighboring objects of an outlier candidate.

We then develop a method, whose solution firmly relies on the matrix eigen-decomposition, to learn an optimal one-dimensional subspace in which an outlier is most distinguishable from its neighboring set.

method

Neighboring Set Selection

Anomaly Degree Computation

Outlier Interpretation

citation

Local Outlier Detection with Interpretation (LODI) [37] and Local Outliers with Graph Projection (LOGP) [38] identify outliers in subspaces of the original feature space and in subspaces of a transformation of the original feature space respectively, like SOD and COP that were introduced in the previous section. But LODI and LOGP provide weights quantifying the importance of each identified feature.

experiment

本文标签： PKDDECMLLocalinterpretationDetection

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