Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensus

2026-07-20

AI Summary

This work addresses the significant performance degradation of multi-view classification under high label noise, where supervision signals become unreliable. To mitigate this issue, the authors propose the Global Anchor Consensus mechanism (GALA), which introduces per-class global anchors shared across views as stable references. By measuring the distances between samples and both their assigned-class and competing-class anchors, and integrating classifier confidence to compute cross-view scrutiny scores, GALA adaptively reweights suspicious samples and corrects their labels. This approach enables noise-robust representation learning and consistently outperforms eight state-of-the-art methods across six benchmark datasets, demonstrating particularly strong performance under high noise rates and validating its effectiveness and robustness.

Abstract

In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views. Most existing multi-view classification methods rely on accurate annotations to guarantee performance. However, noisy labels are ubiquitous in practice due to imperfect annotation, and the refinement signals that existing methods derive from models trained on such noisy supervision can gradually lose their reliability. To deal with this problem, we propose a novel Global Anchor-based Label Auditing method (GALA) for multi-view classification to resist the negative impact of noisy labels. Specifically, we construct a global anchor for each class in every view, which aggregates the samples of the whole class and thus offers a stable reference insensitive to individual predictions. Then, each view measures how close an instance is to the anchor of its observed label relative to the nearest competing anchor, and the per-view evaluations are fused with the classifier confidence into a cross-view audit score. Based on the audit scores, suspicious samples are assigned small weights, and an adaptive correction strategy rewrites a label only when the anchor-based candidate agrees with the classifier prediction. Finally, the corrected labels in turn refine the anchors and supervise noise-robust representation learning. Extensive experiments on six datasets demonstrate that GALA outperforms eight state-of-the-art methods, especially under high noise rates.

Problem

• multi-view classification
• noisy labels
• label noise
• supervision noise
• robust learning

Innovation

• multi-view classification
• noisy labels
• global anchor
• label auditing
• noise-robust learning