Joint Approximate Diagonalization of Eigenmatrices (JADE) Method

This MATLAB-implemented Joint Approximate Diagonalization of Eigenmatrices (JADE) method represents a sophisticated approach to Independent Component Analysis (ICA). As a widely-used signal processing technique, JADE algorithm specializes in separating independent components from mixed signals. The implementation utilizes MATLAB's matrix computation capabilities to perform approximate diagonalization of cumulant matrices, which forms the core of the separation process. Key algorithmic components include: computing fourth-order cumulants to construct characteristic matrices, followed by joint diagonalization through Jacobi rotations to maximize statistical independence. The MATLAB code typically involves functions for covariance matrix whitening, eigenvalue decomposition, and iterative optimization to achieve optimal separation. Through this method, researchers can accurately analyze and process complex signals, significantly enhancing the effectiveness and precision of signal separation tasks. The implementation provides practical insights into ICA principles while demonstrating robust performance in real-world applications such as biomedical signal processing and blind source separation.