Research map / Learning theory and algorithms / Matrix and tensor methods
Stochastic Optimization for Kernel PCA
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase number of random Fourier components from k=5 to k=50 (ablation) | SKPCA · Mushrooms | normalized recovery error convergence | Helped |
| vary regularization lambda (1 vs 10) (ablation) | SKPCA · Mushrooms | convergence of recovery error | No clear effect |
| use SKPCA instead of Nystrom after more training time | kernel PCA · Mushrooms | rank-50 approximation error: outperforms Nystrom after 10 seconds | Helped |
| use SKPCA instead of Kernel Hebbian Algorithm | kernel PCA · Mushrooms | training time | Helped |
Closest papers by meaning
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- Efficient Sparse PCA via Block-Diagonalization (ICLR 2025 · similarity 0.87)
- Rethinking PCA Through Duality (NeurIPS 2025 · similarity 0.86)
- Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity (ICML 2025 · similarity 0.85)
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