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Outlier Detection III: Semi-Supervised Methods

  • Situation: In many applications, the number of labeled data is often small: Labels could be on outliers only, normal objects only, or both
  • Semi-supervised outlier detection: Regarded as applications of semi-supervised learning
  • If some labeled normal objects are available
    • Use the labeled examples and the proximate unlabeled objects to train a model for normal objects
    • Those not fitting the model of normal objects are detected as outliers
  • If only some labeled outliers are available, a small number of labeled outliers many not cover the possible outliers well
    • To improve the quality of outlier detection, one can get help from models for normal objects learned from unsupervised methods


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