Exponential Kernel

Exponential kernels are a class of functions used in machine learning and related fields to measure similarity between data points, particularly within kernel methods. Current research focuses on improving the scalability and efficiency of these kernels, especially for high-dimensional data, through techniques like Fourier feature approximations and novel kernel designs tailored to specific data structures (e.g., tensors, probability measures). These advancements enable the application of kernel methods to larger datasets and more complex problems, impacting fields such as multi-view clustering, Gaussian process regression, and temporal point process modeling.