Compressed Measurement

Compressed measurement focuses on reconstructing high-dimensional signals from a significantly smaller number of measurements, aiming to reduce data acquisition costs and computational burden. Current research emphasizes developing efficient algorithms, including alternating minimization, unrolled neural networks, and various matrix factorization techniques, often incorporating machine learning approaches like reinforcement learning and semi-supervised learning to improve recovery accuracy and robustness. This field is crucial for diverse applications, from signal processing and medical imaging to network analysis and quantum computing, offering significant potential for improving data efficiency and enabling new capabilities in data-intensive domains.