# NIPS Proceedingsβ

## Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features

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### Abstract

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