Singular Value Decomposition (SVD)

Singular Value Decomposition (SVD) is a mathematical method that breaks down a complex matrix (which can represent data, images, or signals) into simpler, fundamental components. It separates the original data into three parts: one that captures patterns or features, one that scales these features (the singular values), and another that shows how these features are oriented. This decomposition helps in noise reduction, data compression, and identifying key patterns, making complex data easier to analyze and interpret while preserving essential information.