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

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning