Dictionary Learning with Matching Pursuit Algorithm

The matching pursuit algorithm based on dictionary learning enables effective sparse representation of signals. This algorithm finds applications not only in signal processing but also extends to various other technical domains. We provide complete MATLAB implementations along with detailed code documentation that demonstrates key functions such as dictionary initialization, atom selection, and residual updating. The algorithm operates by iteratively selecting the most correlated dictionary atoms to represent the signal while minimizing the residual error. Beyond the practical code examples, we explore the theoretical foundations of sparse representation and present real-world application cases across different fields. Through in-depth study and understanding of this algorithm's implementation - including optimization techniques and parameter tuning strategies - you will gain the capability to effectively apply it to solve diverse problems and achieve superior results in related research areas.