Sparse representation theory

Sparse representation theory is a mathematical approach that expresses a complex signal or data set as a combination of a few basic building blocks, called atoms, from a larger set known as a dictionary. The idea is to find the simplest, most efficient way to represent data with as few components as possible, which helps in tasks like noise reduction, compression, and pattern recognition. By focusing on only the most important parts, sparse representation captures the essential information while ignoring redundancies, making data processing more efficient and interpretable.