Feature representation via graph-regularized entropy-weighted nonnegative matrix factorization
Feature extraction plays a crucial role in dimensionality reduction in machine learning applications. Nonnegative Matrix Factorization (NMF) has emerged as a powerful technique for dimensionality reduction; however, its equal treatment of all features may limit accuracy. To address this challenge, t...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Amirkabir University of Technology
2024-10-01
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| Series: | AUT Journal of Mathematics and Computing |
| Subjects: | |
| Online Access: | https://ajmc.aut.ac.ir/article_5535_3112c9212ca8838f81402e7dd4358c84.pdf |
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