juniorAnomaly Detection
What is the Curse of Dimensionality in outlier detection?
Updated May 5, 2026
Short answer
In high dimensions, all points become almost equally distant[cite: 1].
Deep explanation
As dimensions increase, distance-based metrics lose their discriminative power, making every point look like an outlier[cite: 1].
Real-world example
Feature sets with 500+ variables making K-NN unusable[cite: 1].
Common mistakes
- Not performing dimensionality reduction (PCA) before using distance-based models[cite: 1].
Follow-up questions
- How to mitigate this?