Mean Absolute Error

Matrix Decomposition involves breaking down a complex matrix into simpler, constituent components, such as eigenvalues and eigenvectors. This process facilitates easier data manipulation, analysis, and interpretation. It is essential in techniques like Principal Component Analysis (PCA), optimizing algorithms, and reducing computational complexity in artificial intelligence applications, enabling more efficient data processing, pattern recognition, and feature extraction.