SVD (Singular Value Decomposition)

Singular Value Decomposition (SVD) is a mathematical technique that breaks down a complex data matrix into three simpler parts: two smaller matrices and a diagonal matrix with singular values. Think of it as zooming in on the main features of the data, revealing the most important patterns or structures. This process helps in reducing noise, compressing information, and identifying key relationships within the data. Essentially, SVD simplifies complex information while preserving its essential features, making it useful in areas like data analysis, image processing, and machine learning.