Analysis of the robustness of NMF algorithms
This work provides incremental insights into NMF robustness for researchers in machine learning and data analysis, focusing on specific datasets and noise scenarios.
The paper analyzed the robustness of three NMF algorithms (L2-norm, L1-norm, and L2,1-norm) in real-world applications like feature selection, finding that L2,1-norm performed best with lower reconstruction errors and higher accuracy under noise.
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.