ReLoF: Reliability-Guided Logarithmic Fusion for Robust Multi-View Classification

Multi-view learning integrates heterogeneous observations of the same object to improve classification modeling. Despite numerous advances, robustness remains a persistent challenge due to the presence of low-quality or conflicting views. Effectively modeling view reliability in the fused space is therefore nontrivial for robust multi-view classification. This paper proposes Reliability-Guided Logarithmic Fusion (ReLoF), which unifies instance-wise reliability estimation and the logarithmic opinion pool (LogOP) within a categorical probability-opinion framework. In a nutshell, ReLoF first forms a categorical probability opinion for each view, and then estimates its reliability using normalized-entropy confidence and Jensen–Shannon-based inter-view consistency relative to a leave-one-out consensus. Their normalized values serve as LogOP exponents for aggregating class evidence. In addition, ReLoF uses Maximum-Logit (MaxLog) regularization to moderate excessively sharp single-view predictions and mitigate the effect of near-zero true-class probabilities assigned by erroneous, overconfident views. Experimental results on seven benchmark datasets show that ReLoF outperforms baselines remarkably under both normal and conflictive conditions. Ablation studies further show that LogOP improves over conventional weighted averaging and that MaxLog yields consistent numerical improvements under view conflict.