Low-Rank Factorization is a mathematical technique used to approximate a matrix by breaking it down into the product of two or more matrices with lower rank, thereby reducing complexity and computational cost. It's widely applied in data compression, noise reduction, and uncovering latent structures in data across various fields such as machine learning, signal processing, and statistics.