Overview

Description

Feature extraction is about extracting/deriving information from the original features set to create a new features subspace.

The primary idea behind feature extraction is to compress the data to maintain most of the relevant information.

As with feature selection techniques, these techniques are also used for reducing the number of features from the original features set to reduce model complexity, and model overfitting, enhance model computation efficiency, and reduce generalization error.