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.

我们的研究旨在探究非负矩阵分解 (NMF) 在面对不同类型的噪声时的噪声稳健性。通过使用三种不同的 NMF 算法（L1 NMF，L2 NMF 和 L21 NMF）以及 ORL 和 YaleB 数据集进行一系列的模拟实验，分别使用盐和胡椒噪声和块遮挡噪声。实验中，我们使用一系列的评估指标，包括均方根误差（RMSE），准确率（ACC）和归一化互信息（NMI），来评估不同 NMF 算法在噪声环境中的性能。通过这些指标，我们量化了 NMF 算法对噪声的抵抗能力，并深入研究了它们在实际应用中的可行性。