On the space of inner products used in kernel ridge regression, a covariance-weighted gradient flow is proved to converge, preserve rank, and monotonically suppress independent Gaussian noise.
Boumal.An Introduction to Optimization on Smooth Manifolds
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Gradient flow in the kernel learning problem
On the space of inner products used in kernel ridge regression, a covariance-weighted gradient flow is proved to converge, preserve rank, and monotonically suppress independent Gaussian noise.