2017
Large-Scale Data-Dependent Kernel Approximation
AISTATS 2017poster
Learning a computationally efficient kernel from data is an important machine learning problem. The majority of kernels in the literature do not leverage the geometry of the data, and those that do are computationally infeasible for contemporary datasets. Recent advances in approximation techniques…