Parallel Software for Million-scale Exact Kernel Regression
Yu Chen, Lucca Skon, James R. McCombs, Zhenming Liu, Andreas Stathopoulos · 2023
We present the design and the implementation of a kernel principal component regression software that handles training datasets with a million or more observations. Kernel regressions are nonlinear and interpretable models that have wide downstream applications, and are shown to have a close connection to deep learning. Nevertheless, the exact regression of large-scale kernel models using currently available software has been notoriously difficult because it is both compute and memory intensive and it requires extensive tuning of hyperparameters.