An accelerator for kernel ridge regression algorithms based on data partition

Kernel ridge regression (KRR) is an important regression algorithm widely used in pattern recognition and data mining for its interpretability and strong generalization capability. However, it has the defect of low training efficiency when faced with large-scale data. To address this problem， an accelerating algorithm is proposed which uses the concept of divide-and-conquer for kernel ridge regression based on data partition (PP-KRR). Firstly, the current training data space is divided into m mutually disjoint regions by a bunch of parallel hyperplanes. Secondly, each KRR model is trained on each region respectively. Finally, each unlabeled instance is predicted by the KRR model within the same region. Comparisons with three traditional algorithms on real datasets show that the proposed algorithm obtains similar prediction accuracy with less training time.