Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey

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 bridge this gap, this paper presents a comprehensive survey of NMF, focusing on its applications in both feature extraction and feature selection. We propose a novel classification scheme for dimensionality reduction to enhance understanding of its core principles. 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 for leveraging NMF in dimensionality reduction, aiming to highlight areas that need further exploration and development.