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Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey
May 7, 2024, 4:43 a.m. | Farid Saberi-Movahed, Kamal Berahman, Razieh Sheikhpour, Yuefeng Li, Shirui Pan
cs.LG updates on arXiv.org arxiv.org
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
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