Random Laplace Feature Maps for Semigroup Kernels on Histograms

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Scalable Kernel Methods via Doubly Stochastic Gradients (2014, background): “, Hellinger’s, χ(2), exponentiated-χ(2), Jensen-Shannon’s and Intersection kernel, as well as kernels on Abelian semigroups [30].”
Generalization Properties of Learning with Random Features (2016, background): “Random Features Maps for Dot Product Kernels [37, 38, 41] This approximation method is deﬁned for the dot product kernels when X is the ball of R d of radius R , with R > 0 .”
Spherical Random Features for Polynomial Kernels (2015, background): “Another type of kernel that is used widely in many application domains is the polynomial kernel [12, 13], defined by K(x,y) = (〈x,y〉+ q)p, where q is the bias and p is the degree of the polynomial.”
From Dependence to Causation (2016, methodology): “♦ For other examples of shift-invariant kernel approximations using Bochner’s theorem, see Table 1 of (Yang et al., 2014).”
High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization (2014, background): “Random Laplace Features [20] are designed for semigroup kernels that are more suitable for computer vision applications.”