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Challenges for Outlier Detection in High-Dimensional Data

  • Interpretation of outliers
    • Detecting outliers without saying why they are outliers is not very useful in high-D due to many features (or dimensions) are involved in a high-dimensional data set
    • E.g., which subspaces that manifest the outliers or an assessment regarding the “outlier-ness” of the objects
  • Data sparsity
    • Data in high-D spaces are often sparse
    • The distance between objects becomes heavily dominated by noise as the dimensionality increases
  • Data subspaces
    • Adaptive to the subspaces signifying the outliers
    • Capturing the local behavior of data
  • Scalable with respect to dimensionality
    • # of subspaces increases exponentially

Speaker notes:

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