An Example of ICA Algorithm Implementation with Practical Data

This example demonstrates the Independent Component Analysis (ICA) algorithm, specifically designed for beginners. We will perform blind source separation using actual empirical data while thoroughly explaining the underlying principles and operational procedures for each step. Through this practical implementation, you will gain a comprehensive understanding of ICA algorithm applications and working mechanisms. Key implementation aspects include: - Data preprocessing and whitening techniques using eigenvalue decomposition - Implementation of FastICA algorithm with contrast function optimization - Iterative extraction of independent components through fixed-point iteration - Separation matrix calculation and source signal reconstruction The example will showcase practical MATLAB/Python code snippets for covariance matrix computation, centering operations, and convergence criteria checks. Special attention will be given to explaining the non-Gaussianity maximization approach and how the algorithm separates mixed signals without prior knowledge of the source characteristics.