Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey
<|reference_start|>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 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.<|reference_end|>
@article{saberi-movahed2024nonnegative,
    title={Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey},
    author={Farid Saberi-Movahed, Kamal Berahman, Razieh Sheikhpour, Yuefeng Li,
  Shirui Pan},
    journal={arXiv preprint arXiv:2405.03615},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03615},
    primaryClass={cs.LG}
}