Transition Matrix Method with Implicit Regularization for Robust Learning with Label Noise

This paper proposes an innovative and efficient transition matrix method enhanced with implicit regularization for dealing with diverse types of label noise. Label noise can occur in various forms, such as mislabeled samples or corrupted labels, and can significantly affect the performance of machine learning models. Our method aims to mitigate the impact of label noise by incorporating a transition matrix that captures the relationship between clean and noisy labels. We introduce an implicit regularization term to promote the smoothness of the transition matrix, which helps in overcoming the noise in the labels. Additionally, we develop an efficient optimization algorithm to estimate the transition matrix, which significantly reduces the computational complexity compared to existing methods. Experimental results on various datasets demonstrate the effectiveness and efficiency of our proposed method in handling different types of label noise. Overall, our approach provides a promising solution for improving the robustness of machine learning models in the presence of label noise.