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arXiv · 1808.00934

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

Enayat Ullah·Poorya Mianjy·Teodor V. Marinov·Raman Arora
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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.

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Enayat Ullah, Poorya Mianjy, Teodor V. Marinov, Raman Arora. 2018-08-02. Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features. https://arxiv.org/abs/1808.00934

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