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Poster
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Enayat Ullah · Poorya Mianjy · Teodor Vanislavov Marinov · Raman Arora
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
Author Information
Enayat Ullah (Johns Hopkins University)
Poorya Mianjy (Johns Hopkins University)
Teodor Vanislavov Marinov (Johns Hopkins University)
Raman Arora (Johns Hopkins University)
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