Curse of Dimensionality

The Curse of Dimensionality is a phenomenon that arises in high-dimensional datasets(datasets with many features), where the number of data points needed to accurately capture the relationships between features grows exponentially with the number of features.


High-Dimensionality will cause several problems:

  • With higher number of features, errors grow as well.
  • The number of data points required for machine learning training increases exponentially.
  • Designing machine learning modes becomes more complex, as it becomes harder to distinguish patterns from the noise.

Three most effected areas by the Curse of Dimensionality are:

Resolving the Curse of Dimensionality: