Research map / Learning theory and algorithms / Matrix and tensor methods
How many samples is a good initial point worth in Low-rank Matrix Recovery?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use an initial point with smaller relative error from the ground truth | Matrix sensing recovery · ε-neighborhood of the solution | sample complexity | Helped |
| Use an initial point whose column space is more aligned with the ground truth | Matrix sensing recovery · Optimization landscape near the ground truth | sample complexity to prevent spurious critical points | Helped |
| Start from a point whose column space is close to orthogonal to the ground truth | Matrix sensing recovery · Optimization landscape away from the ground truth | sample complexity to prevent spurious critical points | Hurt |
Closest papers by meaning
- Global Optimality of Local Search for Low Rank Matrix Recovery (NeurIPS 2016 · similarity 0.91)
- Sharp Restricted Isometry Property Bounds for Low-Rank Matrix Recovery Problems with Corrupted Measurements (AAAI 2022 · similarity 0.91)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.90)
- Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction (NeurIPS 2021 · similarity 0.90)
- How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? (NeurIPS 2018 · similarity 0.90)
- Fast exact recovery of noisy matrix from few entries: the infinity norm approach (NeurIPS 2025 · similarity 0.88)
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