Independent Component Analysis
Independent Component Analysis (ICA) is a powerful data analysis tool that has emerged in recent years. It was first mathematically defined by Comon in 1994, building upon concepts originally introduced by Herault and Jutten in 1986. Despite its relatively recent development, ICA has gained significant theoretical and practical attention globally, becoming a prominent research focus. Its implementation typically involves optimization algorithms like FastICA or InfoMax to separate statistically independent source signals from mixed observations. Applications span blind source separation, image processing, speech recognition, biomedical signal processing, and financial data analysis, making it an extension of Principal Component Analysis (PCA) with broader independence constraints.