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 random-feature coordinates with an Adam-style orthonormal basis update for an established objective. Fixed-map consistency, concentration and perturbation results describe the estimator and its exact subspace; same-target comparisons then assess the practical iterate separately. Across six predictive benchmarks, performance depends on the declared pipeline: replacing the tracker with the exact empi...
| Authors | Zhenlin Yao, Wei Xiong |
|---|---|
| Primary category | stat.ML |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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