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Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs
Accurate optimization of a supervised spectral objective need not produce an accurate population subspace or a better predictive representation. We investigate these distinctions for Online Kernel Supervised Principal Component Analysis (OKSPCA), which combines a centered cross-moment in finite rand
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