Matrix Decomposition is a mathematical technique used to break down a complex matrix into simpler, more manageable components. This process helps in understanding the structure of the matrix and can simplify various computations, such as solving systems of equations or performing data analysis. Common types of Matrix Decomposition include LU Decomposition, which separates a matrix into a lower triangular matrix and an upper triangular matrix, and Singular Value Decomposition (SVD), which expresses a matrix in terms of its singular values and vectors. These methods are widely used in fields like machine learning, statistics, and computer graphics.