An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.
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Short-time rescalings of compression covariance defects E_s,t = V_s^* V_t yield tangent kernels F whose Kolmogorov spaces carry induced contraction semigroups whose representing vectors obey additive cocycle identities, restricting admissible positive kernels.
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Infinite-Dimensional Spherical Kernel ridge Regression
An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.
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Compression Covariance and Tangent kernels
Short-time rescalings of compression covariance defects E_s,t = V_s^* V_t yield tangent kernels F whose Kolmogorov spaces carry induced contraction semigroups whose representing vectors obey additive cocycle identities, restricting admissible positive kernels.