Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?

NeurIPS 2017 Cameron MuscoDavid P. Woodruff

Low-rank approximation is a common tool used to accelerate kernel methods: the $n \times n$ kernel matrix $K$ is approximated via a rank-$k$ matrix $\tilde K$ which can be stored in much less space and processed more quickly. In this work we study the limits of computationally efficient low-rank kernel approximation... (read more)

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