Random Sketching

Random sketching is a technique that uses random projections to reduce the dimensionality of large datasets, enabling faster and more efficient computation for various machine learning and numerical linear algebra tasks. Current research focuses on developing sketching methods optimized for specific problems, such as least squares regression and nonparametric estimation, often incorporating techniques like variance reduction and bias minimization to improve accuracy. These advancements are significantly impacting fields like signal processing and imaging, accelerating algorithms for solving large-scale linear systems and improving the efficiency of deep learning models, particularly in high-dimensional settings.