Matrix Pre-orthogonal-Matching Pursuit as a Fundamental AI Algorithm

We develop a framework for efficient sparse representation and approximation in artificial intelligence called Matrix Pre-orthogonal Matching Pursuit. By leveraging matrix structures and pre-orthogonalization, the method enhances convergence and reduces computational complexity. This approach offers potential benefits in signal processing, data compression, and machine learning, with promising results demonstrated through preliminary experiments.