Kernel Matrix

Kernel matrices represent the pairwise similarities between data points, forming the foundation for many machine learning algorithms, particularly kernel methods. Current research focuses on developing efficient algorithms to handle the computational challenges associated with large kernel matrices, including fast matrix-vector products, low-rank approximations, and the use of structured matrices and sketching techniques to reduce complexity. These advancements are crucial for scaling kernel methods to larger datasets and improving their applicability in diverse fields like spatial statistics, time series analysis, and graph-based learning, ultimately enhancing the efficiency and scalability of numerous machine learning applications.