Sparse Approximation

Sparse approximation focuses on representing data using a minimal number of non-zero components, aiming to reduce computational complexity and improve efficiency in various applications. Current research emphasizes developing faster algorithms like improved Orthogonal Matching Pursuit and leveraging sparse structures within models such as Gaussian Processes and neural networks (including Binary Neural Networks and Vision Transformers) for enhanced scalability and accuracy. This field is crucial for addressing computational bottlenecks in machine learning, signal processing, and scientific computing, enabling the analysis of high-dimensional data and the deployment of complex models on resource-constrained devices.