ITM Web Conf.
Volume 48, 2022The 4th International Conference on Computing and Wireless Communication Systems (ICCWCS 2022)
|Number of page(s)||5|
|Section||Computer Science, Intelligent Systems and Information Technologies|
|Published online||02 September 2022|
Non-negative Matrix Factorization for Dimensionality Reduction
New Technology Trends (NTT)
National School of Applied Sciences
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.