We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game. Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.
A simple and practical algorithm for learning a margin-maximizing translation-invariant or spherically symmetric kernel from training data, using tools from Fourier analysis and regret minimization. The paper proposes to learn a custom translation or rotation invariant kernal in the Fourier representation to maximize the margin of SVM. The authors propose an interesting algorithm for learning the l1-SVM and the Fourier represented kernel together The authors consider learning directly Fourier representations of shift/translation invariant kernels for machine learning applications with the alignment of the kernel to data as the objective function to optimize.