Dimensionality reduction

Dimensionality reduction is the process of reducing the number of variables under consideration in a dataset. It is useful for compression and identifying underlying structure. Key techniques:

Purposes include:

Challenges involve preserving essential information when projecting to lower dimensions. Many linear and nonlinear techniques provide alternative approaches to dimensionality reduction.

The process is critical for machine learning pipelines to avoid overfitting and the curse of dimensionality. It enables deriving insights from complex high-dimensional data.

See also: