Complementarity Information

Complementarity information, in machine learning and related fields, refers to the synergistic value derived from combining diverse data sources or perspectives that offer partially overlapping, yet distinct, insights. Current research focuses on developing methods that effectively balance this complementarity with consistency across different views, often employing techniques like dual networks with delayed activation, attention mechanisms for feature regularization, and graph-based approaches for optimal collaboration selection in federated learning settings. This research is significant because effectively leveraging complementarity improves model performance, robustness, and efficiency across various applications, including multi-view clustering, causal discovery, and few-shot learning, particularly in scenarios with incomplete or heterogeneous data.