  Dimensionality Reduction plays a pivotal role in improving feature learning
accuracy and reducing training time by eliminating redundant features, noise,
and irrelevant data. Nonnegative Matrix Factorization (NMF) has emerged as a
popular and powerful method for dimensionality reduction. Despite its extensive
use, there remains a need for a comprehensive analysis of NMF in the context of
dimensionality reduction. To address this gap, this paper presents a
comprehensive survey of NMF, focusing on its applications in both feature
extraction and feature selection. We introduce a classification of
dimensionality reduction, enhancing understanding of the underlying concepts.
Subsequently, we delve into a thorough summary of diverse NMF approaches used
for feature extraction and selection. Furthermore, we discuss the latest
research trends and potential future directions of NMF in dimensionality
reduction, aiming to highlight areas that need further exploration and
development.
