Kernel Based Representation

Kernel-based representations are a powerful tool in machine learning, aiming to efficiently capture complex relationships within data by mapping it into a higher-dimensional feature space defined by a kernel function. Current research focuses on improving the scalability and efficiency of these methods, particularly through sparse representations in the Fourier domain and adaptive kernel learning tailored to specific data structures like graphs. These advancements are leading to improved performance in various applications, including graph classification, image recognition, and text detection, by enabling faster training and inference while maintaining or improving accuracy.