Matrix analysis
Fourier analysis and applications: filtering, numerical computation, wavelets
Fourier analysis and applications: filtering, numerical computation, wavelets
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Refinement of Reproducing Kernels
The Journal of Machine Learning Research
Fast discrete algorithms for sparse Fourier expansions of high dimensional functions
Journal of Complexity
Reproducing Kernel Banach Spaces for Machine Learning
The Journal of Machine Learning Research
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Kernels are important in developing a variety of numerical methods, such as approximation, interpolation, neural networks, machine learning and meshless methods for solving engineering problems. A common problem of these kernel-based methods is to calculate inverses of kernel matrices generated by a kernel function and a set of points. Due to the denseness of these matrices, finding their inverses is computationally costly. To overcome this difficulty, we introduce in this paper an approximation of the kernel matrices by appropriate multilevel circulant matrices so that the fast Fourier transform can be applied to reduce the computational cost. Convergence analysis for the proposed approximation is established based on certain decay properties of the kernels.