Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) is a statistical method used to identify and quantify the linear relationships between two or more sets of variables. Current research focuses on extending CCA to handle nonlinear relationships using deep learning architectures (like Deep CCA) and addressing challenges like high-dimensionality, unpaired data, and the need for improved scalability and interpretability. These advancements are impacting diverse fields, including multimodal data analysis (e.g., neuroimaging, genomics), machine learning model fusion, and signal processing, by enabling more effective integration and analysis of complex datasets.