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

Poster

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

Enayat Ullah · Poorya Mianjy · Teodor Vanislavov Marinov · Raman Arora

Keywords: [ Kernel Methods ] [ Learning Theory ]

[ Abstract ]

[ Paper] [ Poster]

2018 Poster

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 / ϵ^{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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Poster

Streaming Kernel PCA with O~(n) Random Features

Enayat Ullah · Poorya Mianjy · Teodor Vanislavov Marinov · Raman Arora

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