Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Ullah, Md Enayat, Mianjy, Poorya, Marinov, Teodor Vanislavov, Arora, Raman
–Neural Information Processing Systems
We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve $O(1/\epsilon^2)$ sample complexity. Furthermore, we give a memory efficient streaming algorithm based on classical Oja's algorithm that achieves this rate
Neural Information Processing Systems
Dec-31-2018