Coordinated Sparse Recovery of Label Noise

• URL: http://arxiv.org/abs/2404.04800v1
• Date: Sun, 7 Apr 2024 03:41:45 GMT
• Title: Coordinated Sparse Recovery of Label Noise
• Authors: Yukun Yang, Naihao Wang, Haixin Yang, Ruirui Li,
• Abstract summary: This study focuses on robust classification tasks where the label noise is instance-dependent. We propose a method called Coordinated Sparse Recovery (CSR) CSR introduces a collaboration matrix and confidence weights to coordinate model predictions and noise recovery, reducing error leakage. Based on CSR, this study designs a joint sample selection strategy and constructs a comprehensive and powerful learning framework called CSR+.
• Score: 2.9495895055806804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Label noise is a common issue in real-world datasets that inevitably impacts the generalization of models. This study focuses on robust classification tasks where the label noise is instance-dependent. Estimating the transition matrix accurately in this task is challenging, and methods based on sample selection often exhibit confirmation bias to varying degrees. Sparse over-parameterized training (SOP) has been theoretically effective in estimating and recovering label noise, offering a novel solution for noise-label learning. However, this study empirically observes and verifies a technical flaw of SOP: the lack of coordination between model predictions and noise recovery leads to increased generalization error. To address this, we propose a method called Coordinated Sparse Recovery (CSR). CSR introduces a collaboration matrix and confidence weights to coordinate model predictions and noise recovery, reducing error leakage. Based on CSR, this study designs a joint sample selection strategy and constructs a comprehensive and powerful learning framework called CSR+. CSR+ significantly reduces confirmation bias, especially for datasets with more classes and a high proportion of instance-specific noise. Experimental results on simulated and real-world noisy datasets demonstrate that both CSR and CSR+ achieve outstanding performance compared to methods at the same level.

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