Overview

Unsupervised feature selection aims to identify features that capture the underlying structure of unlabeled data, facilitating tasks such as clustering, dimensionality reduction, and manifold learning. Without the guidance of a target variable, these methods must rely on the data’s intrinsic properties to determine feature relevance.