Matrix Decomposition

Matrix decomposition is a mathematical technique used to factorize a matrix into a product of matrices, simplifying complex matrix operations. It has several forms, including Singular Value Decomposition (SVD), Eigenvalue Decomposition, and LU Decomposition, each serving different purposes in data analysis and machine learning. This method is crucial for tasks such as dimensionality reduction, data compression, and solving linear systems efficiently. Common use cases include recommendation systems, image processing, and natural language processing, where it helps to uncover latent structures in data.