arXiv Statistics ML By Xabier de Juan, Santiago Mazuelas, Yilun Zhu, Clayton Scott

Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification

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arXiv Machine Learning
Sep 10

Selective Posterior Margin Regularization for Forward-Corrected Classification

The paper introduces Selective Posterior Margin Regularization (SPMR), a technique that enhances Forward correction for learning with class‑conditional label noise. SPMR preserves the Forward objective while converting disagreements between the corrected likelihood’s reverse posterior and the observed annotation into a graded update on the clean classifier. Experiments on five known‑transition benchmarks show that SPMR improves performance by 2.5–7.0 percentage points over full‑length Forward and remains 0.7–2.5 percentage points better when combined with Mixup and early stopping, with gains attributed to posterior‑space coefficients, transition‑adjusted targets, and pairwise actions.

By Zexing Zhang, Jichao Li, Tianyang Lei, XiongYi Lu, Yang Kewei