Laplacian Kernel

The Laplacian kernel is a function used in machine learning to measure similarity between data points, particularly effective in scenarios involving non-Euclidean data or complex relationships. Current research focuses on its application within various algorithms, including kernel ridge regression, diffusion models, and distributed/quantum machine learning frameworks, often comparing its performance against Gaussian kernels and neural tangent kernels. This versatile kernel finds applications in diverse fields such as image processing, fluid dynamics modeling, and functional data analysis, improving the robustness and efficiency of existing methods. The ongoing exploration of its properties and applications continues to refine our understanding of kernel methods and their potential for solving complex problems.