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Non-negative matrix factorization (NMF) and its variants have been widelyemployed in clustering and classification tasks (Long, & Jian , 2021). However,noises can seriously affect the results of our experiments. Our research isdedicated to investigating the noise robustness of non-negative matrixfactorization (NMF) in the face of different types of noise. Specifically, weadopt three different NMF algorithms, namely L1 NMF, L2 NMF, and L21 NMF, anduse the ORL and YaleB data sets to simulate a series of experiments withsalt-and-pepper noise and Block-occlusion noise separately. In the experiment,we use a variety of evaluation indicators, including root mean square error(RMSE), accuracy (ACC), and normalized mutual information (NMI), to evaluatethe performance of different NMF algorithms in noisy environments. Throughthese indicators, we quantify the resistance of NMF algorithms to noise andgain insights into their feasibility in practical applications.

Analyze the robustness of three NMF algorithms (Robust NMF with L1 norm, L2-1 norm NMF, L2 NMF)