# Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation

@article{Bakshi2022SubquadraticAF, title={Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation}, author={Ainesh Bakshi and Piotr Indyk and Praneeth Kacham and Sandeep Silwal and Samson Zhou}, journal={ArXiv}, year={2022}, volume={abs/2212.00642} }

Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in machine learning, statistics and other related fields. The main drawback of using kernel methods (learning and inference using kernel matrices) is efficiency – given n input points, most kernel-based algorithms need to materialize the full n× n kernel matrix before performing any subsequent computation, thus incurring Ω(n2) runtime. Breaking this quadratic barrier for various problems has therefore, been…

## One Citation

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