Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey

arXiv:2405.03615v1 Announce Type: new
Abstract: 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 …

abstract accuracy arxiv cs.lg data dimensionality factorization feature features improving matrix noise pivotal popular role survey training type