Robust Multi-View Learning via Hybrid Geometric Reliability under Test-Time Noisy Correspondence

The effectiveness of multi-view learning hinges on correct correspondences across different views. However, data acquisition and transmission failures in practical deployment may cause certain views of a sample to be mismatched, leading to test-time noise correspondence that severely undermines multi-view fusion. Existing methods predominantly rely on view-wise statistical information for reliability assessment, while largely overlooking the intrinsic geometric structure of multi-view features. To address these limitations, we propose a robust multi-view learning framework based on Hybrid Geometric Reliability, termed HGR. First, Noisy Correspondence Augmented Learning simulates diverse view mismatch patterns during training and learns view reliability through explicit supervision. Second, Hybrid Geometric Learning constructs Euclidean and Hyperbolic Gramian matrices to capture the high-order joint geometric structure of multiple views, and performs reliability evaluation based on cross-view structural relationships. Furthermore, the Geometric Consistency Principle is introduced, where the Gramian volume contrastive loss and the view consistency loss are devised. The two losses reinforce the geometric coherence of correctly matched samples while amplifying the structural distortion of mismatched ones. Extensive experiments on multiple multi-view benchmark datasets demonstrate that the proposed HGR achieves superior performance and robustness over the state-of-the-art methods.