Analyze the Robustness of Classifiers under Label Noise

This study explores the robustness of label noise classifiers, aiming to enhance model resilience against noisy data in complex real-world scenarios. Label noise in supervised learning, characterized by erroneous or imprecise labels, significantly impairs model performance. This research focuses on the increasingly pertinent issue of label noise's impact on practical applications. Addressing the prevalent challenge of inaccurate training data labels, we integrate adversarial machine learning (AML) and importance reweighting techniques. Our approach involves employing convolutional neural networks (CNN) as the foundational model, with an emphasis on parameter adjustment for individual training samples. This strategy is designed to heighten the model's focus on samples critically influencing performance.

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

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.