Anomaly Detection

By Prof. Seungchul Lee
Industrial AI Lab at KAIST

Table of Contents

1. AnomalyΒΆ

  • Anomalies and outliers are essentially the same thing
    • Objects that are different from most other objects
    • Something that deviates from what is standard, or expected (one classification)

Causes of Anomalies

  • Data from different class of object or underlying mechanism
    • disease vs. non-disease
    • fraud vs. not fraud
  • Data measurement and collection errors
  • Natural variation
    • tails on a Gaussian distribution

$$P_Y(Y=y) = \frac{1}{\sqrt{2\pi}} \exp \left(-\frac{1}{2} y^2 \right)$$

Anomaly Detection

  • Finding outliers