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تسجيل مستخدم جديدStreaming Kernel PCA with $tilde{O}(sqrt{n})$ Random Features
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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 Ojas algorithm that achieves this rate.
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