Dynamic Quality Assessment-Based Multi-Feature Fusion
Qilin Li, Yiyu Gong, Jungang You, Hongbin Hu, Chuan Peng, Dongxue Peng, Yi Wang · Electronics · 2026
To address the challenge in multi-view learning within practical application scenarios—such as smart grid multi-source monitoring and complex environment perception—where view quality often exhibits significant dynamic time-varying characteristics due to environmental interference or sensor failures, rendering traditional static fusion methods inadequate for maintaining decision-making reliability, a general adaptive robust fusion method, termed the Consensus-Aware Residual Gating (CARG) mechanism, is proposed. This approach constructs a sample-level dynamic quality assessment framework. It computes three interpretable metrics—self-confidence, group consensus, and complementary uniqueness—for each feature view in real time, thereby accurately quantifying instantaneous data quality fluctuations. A multiplicative gating structure is employed to generate dynamic weights based on these metrics, embedding a structural inductive bias of group consensus priority. Specifically, when quality degradation triggers view conflicts, the mechanism prioritizes majority-consistent reliable signals to suppress noise; when high-value complementary information emerges, it cautiously incentivizes discriminative features to rectify group bias. This design achieves adaptive perception of quality variations and robust decision-making without relying on additional weight-prediction networks. Extensive experiments are conducted on general multi-view benchmarks. The results demonstrate that CARG surpasses mainstream algorithms in accuracy, robustness, and interpretability. It effectively shields decisions from anomalous feature interference and validates its efficacy as a universal fusion framework for dynamic environments.