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

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

Authors

• Enayat Ullah
• Poorya Mianjy
• Teodor Vanislavov Marinov
• Raman Arora Johns Hopkins University

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