Analysis of the robustness of NMF algorithms
- URL: http://arxiv.org/abs/2106.02213v1
- Date: Fri, 4 Jun 2021 02:35:24 GMT
- Title: Analysis of the robustness of NMF algorithms
- Authors: Alex D\'iaz, Damian Steele
- Abstract summary: We examine three non-negative matrix factorization techniques; L2-norm, L1-norm, and L2,1-norm.
Our aim is to establish the performance of these different approaches, and their robustness in real-world applications.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We examine three non-negative matrix factorization techniques; L2-norm,
L1-norm, and L2,1-norm. Our aim is to establish the performance of these
different approaches, and their robustness in real-world applications such as
feature selection while managing computational complexity, sensitivity to noise
and more. We thoroughly examine each approach from a theoretical perspective,
and examine the performance of each using a series of experiments drawing on
both the ORL and YaleB datasets. We examine the Relative Reconstruction Errors
(RRE), Average Accuracy and Normalized Mutual Information (NMI) as criteria
under a range of simulated noise scenarios.
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