Higher Order Correlation

Higher-order correlation analysis focuses on uncovering relationships between three or more variables, going beyond the pairwise correlations typically considered. Current research investigates efficient methods for extracting information from these higher-order correlations, particularly within neural networks and other machine learning models, often employing techniques like Independent Component Analysis and tensor-based methods. This research is significant because it allows for a more nuanced understanding of complex systems by capturing intricate dependencies missed by simpler approaches, leading to improved model performance and interpretability in diverse fields such as quantum chemistry and multi-view learning.