SVD, or "singular value decomposition," can also be referred to as "singular spectrum analysis" or "principal component analysis." These terms essentially describe the same mathematical process of breaking down a data matrix into its constituent components to reveal underlying patterns and structures. Other related concepts include "eigenvalue decomposition" and "factor analysis," which involve similar methods of extracting meaningful information from large datasets. Regardless of the specific terminology used, SVD is a powerful tool for data analysis and has applications across a wide range of fields, from natural language processing to image recognition to finance.