Research map / Learning theory and algorithms / Matrix and tensor methods
Regularized Weighted Low Rank Approximation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CountSketch during alternating minimization | Regularized weighted low rank approximation · landmark and NIPS | runtime: 54.31 vs 126.22; 49.1 vs 104.5 (t=10) | Helped |
| Use CountSketch during alternating minimization | Regularized weighted low rank approximation · landmark and NIPS | objective value: within a factor of 1.5 | Mixed |
| Reduce the sketch size t below the rank parameter k (ablation) | Alternating Minimization with Sketching · connectus, NIPS, and synthetic datasets | objective value | Mixed |
| Restrict factor dimensions through sketching (ablation) | Alternating minimization heuristic · regularized weighted low rank approximation | objective value | Helped |
Closest papers by meaning
- Additive Error Guarantees for Weighted Low Rank Approximation (ICML 2021 · similarity 0.92)
- Approximation Algorithms for $\ell_0$-Low Rank Approximation (NeurIPS 2017 · similarity 0.91)
- Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation (ICLR 2025 · similarity 0.91)
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time (ICLR 2024 · similarity 0.89)
- Reweighted Solutions for Weighted Low Rank Approximation (ICML 2024 · similarity 0.88)
- Entrywise error bounds for low-rank approximations of kernel matrices (NeurIPS 2024 · similarity 0.87)
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