Sparse Representation

Sparse representation aims to efficiently represent data using a minimal number of non-zero components, improving computational efficiency and interpretability. Current research focuses on developing novel algorithms, such as variations of Orthogonal Matching Pursuit and Iterative Shrinkage Thresholding Algorithm (ISTA), and incorporating sparse representations into diverse model architectures, including deep learning models and graph-based methods, for applications like anomaly detection, signal reconstruction, and image processing. This approach is proving valuable across numerous fields, enhancing the performance and scalability of machine learning models while addressing challenges related to high dimensionality and computational cost in various applications. The resulting sparse models offer advantages in terms of speed, memory efficiency, and interpretability, leading to improvements in accuracy and robustness.