  Non-negative matrix factorisation (NMF) has been extensively applied to the
problem of corrupted image data. Standard NMF approach minimises Euclidean
distance between data matrix and factorised approximation. The traditional NMF
technique is sensitive to outliers since it utilises the squared error of each
data point, despite the fact that this method has proven effective. In this
study, we theoretically examine the robustness of the traditional NMF, HCNMF,
and L2,1-NMF algorithms and execute sets of experiments to demonstrate the
robustness on ORL and Extended YaleB datasets. Our research indicates that each
algorithm requires a different number of iterations to converge. Due to the
computational cost of these approaches, our final models, such as the HCNMF and
L2,1-NMF model, fail to converge within the iteration parameters of this work.
Nonetheless, the experimental results illustrate, to some extent, the
robustness of the aforementioned techniques.
