Solving Kernel Ridge Regression with Gradient-Based Optimization Methods

arXiv:2306.16838v5 Announce Type: replace-cross
Abstract: Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the parameters. Here, we introduce an equivalent formulation of the objective function of KRR, opening up both for using penalties other than the ridge penalty and for studying kernel ridge regression from the perspective of gradient descent. Using a continuous-time perspective, we derive a closed-form solution for solving kernel regression with gradient descent, something we …

abstract arxiv cs.lg data function gradient kernel linear math.oc non-linear optimization parameters regression ridge stat.me stat.ml type