LapRamp: a Noise Resistant Classification Algorithm Based on Manifold Regularization

Abstract: In many applications, data samples contain incorrect labels due to data quality issues and the high cost of labeling. Although current noise-resistant classification algorithms can handle specific types of label noise, identifying the type of noise present in the given data is challenging. To address this issue, we propose a robust classification method called LapRamp , which works with multiple types of label noise. LapRamp utilizes the ramp loss function to minimize the impact of mislabeled samples far from the discriminant surface. Additionally, we incorporate manifold regularization to capture the inherent geometric structure of the data. We analyze the generalization error bound of the model in terms of Rademacher complexity, and the preliminary experimental results indicate that LapRamp has good generalization performance despite the presence of mixed label noise. Furthermore, it demonstrates stable classification accuracy when dealing with noisy labels in various scenarios.