Block Basis Factorization for Scalable Kernel Evaluation
Block Basis Factorization for Scalable Kernel Evaluation
Kernel methods are widespread in machine learning; however, they are limited by the quadratic complexity of the construction, application, and storage of kernel matrices. Low-rank matrix approximation algorithms are widely used to address this problem and reduce the arithmetic and storage cost. However, we observed that for some datasets with …