Large Scale Kernel

Large-scale kernel methods aim to apply the power of kernel techniques to massive datasets, overcoming the computational limitations of traditional approaches. Current research focuses on developing efficient approximations, such as random Fourier features and random walks on graphs, and integrating deep learning architectures like Kolmogorov-Arnold networks to create more expressive and scalable kernel models. These advancements enable the application of kernel methods to previously intractable problems in various fields, improving accuracy and interpretability in machine learning tasks while reducing computational costs.