Random Fourier Feature

Random Fourier features (RFFs) are a powerful technique for approximating kernel functions, enabling the efficient application of kernel methods to large datasets. Current research focuses on improving the accuracy and efficiency of RFF approximations, exploring variations like orthogonal RFFs and adaptive algorithms, and integrating RFFs into diverse models such as neural networks and Gaussian processes. This work addresses the computational bottleneck inherent in many kernel methods, impacting fields ranging from machine learning and signal processing to scientific computing by enabling the use of powerful kernel-based techniques on previously intractable datasets.