Analyze the robustness of three NMF algorithms (Robust NMF with L1 norm,
  L2-1 norm NMF, L2 NMF)
Cheng Zeng Jiaqi Tian Yixuan Xu
Non-negative matrix factorization (NMF) and its variants have been widely
employed in clustering and classification tasks (Long, & Jian , 2021). However,
noises can seriously affect the results of our experiments. Our research is
dedicated to investigating the noise robustness of non-negative matrix
factorization (NMF) in the face of different types of noise. Specifically, we
adopt three different NMF algorithms, namely L1 NMF, L2 NMF, and L21 NMF, and
use the ORL and YaleB data sets to simulate a series of experiments with
salt-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 evaluate
the performance of different NMF algorithms in noisy environments. Through
these indicators, we quantify the resistance of NMF algorithms to noise and
gain insights into their feasibility in practical applications.