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• W2365516273 abstract "Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel Hilbert Spaces. To scale up these methods, we extend the celebrated Random Fourier Feature methodology to get an approximation of operator-valued kernels. We propose a general principle for Operator-valued Random Fourier Feature construction relying on a generalization of Bochner's theorem for translation-invariant operator-valued Mercer kernels. We prove the uniform convergence of the kernel approximation for bounded and unbounded operator random Fourier features using appropriate Bernstein matrix concentration inequality. An experimental proof-of-concept shows the quality of the approximation and the efficiency of the corresponding linear models on example datasets." @default.
• W2365516273 created "2016-06-24" @default.
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• W2365516273 date "2016-05-09" @default.
• W2365516273 modified "2023-10-01" @default.
• W2365516273 title "Random Fourier Features for Operator-Valued Kernels" @default.
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