The transition matrix, denoting the transition relationship from clean labels to noisy labels, is essential to build statistically consistent classifiers in label-noise learning. Existing methods for estimating the transition matrix rely heavily on estimating the noisy class posterior. However, the estimation error for noisy class posterior could be large due to the randomness of label noise, which would lead the transition matrix to be poorly estimated. Therefore, in this paper, we aim to solve this problem by exploiting the divide-and-conquer paradigm. Specifically, we introduce an intermediate class to avoid directly estimating the noisy class posterior. By this intermediate class, the original transition matrix can then be factorized into the product of two easy-to-estimate transition matrices. We term the proposed method the dual-T estimator. Both theoretical analyses and empirical results illustrate the effectiveness of the dual-T estimator for estimating transition matrices, leading to better classification performances.

翻译：过渡矩阵指出从清洁标签到吵闹标签的过渡关系,对于在标签噪音学习中建立统计上一致的分类系统至关重要。现有的过渡矩阵估算方法主要依靠对吵闹的阶级后背体进行估计。然而,由于标签噪音的随机性,对吵闹的阶级后背体的估计误差可能很大,导致过渡矩阵估计不力。因此,在本文件中,我们的目标是通过利用鸿沟和征服模式来解决这一问题。具体地说,我们引入一个中间类以避免直接估计吵闹的阶级后背体。在这个中间类中,最初的过渡矩阵可以乘入两个容易估计的过渡矩阵的产物。我们将拟议的方法称为双重估计。理论分析和经验结果都说明了双重估计过渡矩阵的有效性,导致更好的分类表现。