| Issue |
ITM Web Conf.
Volume 48, 2022
The 4th International Conference on Computing and Wireless Communication Systems (ICCWCS 2022)
|
|
|---|---|---|
| Article Number | 03006 | |
| Number of page(s) | 5 | |
| Section | Computer Science, Intelligent Systems and Information Technologies | |
| DOI | https://doi.org/10.1051/itmconf/20224803006 | |
| Published online | 02 September 2022 | |
Non-negative Matrix Factorization for Dimensionality Reduction
New Technology Trends (NTT)
National School of Applied Sciences
Tetuan, Morocco
olaya.jbari@etu.uae.ac.ma
otman.chakkor@uae.ac.ma
Abstract—What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of matrix factorization. We discuss the main optimization algorithms, used to solve the NMF problem, and their convergence. The paper also contains a comparative study between principal component analysis (PCA), independent component analysis (ICA), and NMF for dimensionality reduction using a face image database.
Index Terms—NMF, PCA, ICA, dimensionality reduction.
© The Authors, published by EDP Sciences, 2022
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