Research map / Learning theory and algorithms / Matrix and tensor methods
Generative Principal Component Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use projected power iterations constrained to the range of a generative model instead of vanilla power iterations | Power · MNIST spiked covariance | Cosine Similarity | Helped |
| Use projected power iterations constrained to the range of a generative model instead of truncated power iterations | TPower · MNIST spiked covariance | Cosine Similarity | Helped |
| Use projected power iterations constrained to the range of a generative model instead of vanilla power iterations | Power · MNIST phase retrieval | Cosine Similarity | Helped |
| Use projected power iterations constrained to the range of a generative model instead of truncated power iterations | TPower · MNIST phase retrieval | Cosine Similarity | Helped |
Closest papers by meaning
- Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors (NeurIPS 2020 · similarity 0.90)
- Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity (ICML 2025 · similarity 0.86)
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- The Generative Leap: Tight Sample Complexity for Efficiently Learning Gaussian Multi-Index Models (NeurIPS 2025 · similarity 0.85)
- Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors (AAAI 2024 · similarity 0.84)
- Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective (NeurIPS 2024 · similarity 0.84)
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