Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs

arXiv:2609.20454 · stat.ML, cs.LG, stat.CO · Submitted 2026-09-17 · Read on arXiv

stat.ML, cs.LG, stat.CO

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 41 pages, 4 figures, 18 tables; includes core supplementary material

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adam — 54× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Online Kernel Supervised Principal Component Analysis — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• Stiefel manifold — 1× in this paper · explained in 2 episodes

Sources

• Covariance Supervised Principal Component Analysis
• SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections
• Online Supervised Subspace Tracking

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