Singular Value Decomposition

Singular Value Decomposition (SVD) is a fundamental linear algebra technique used to decompose matrices into constituent components, revealing underlying structure and facilitating dimensionality reduction. Current research focuses on applying SVD and its variants (e.g., truncated SVD, generalized SVD) within diverse machine learning contexts, including neural network compression, recommendation systems, and time-series analysis, often in conjunction with other methods like Koopman operators or matrix factorization. These applications aim to improve computational efficiency, enhance model robustness, and achieve better performance in tasks ranging from image generation to scientific modeling. The widespread use of SVD underscores its importance as a powerful tool for data analysis and model optimization across numerous scientific and engineering disciplines.