Research map / Learning theory and algorithms / Matrix and tensor methods
Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use hierarchical RANSAC with minimal solvers instead of the compared methods | Low-rank matrix factorization · Dinosaur ~300 pts | Frobenius error: 28.0 -> 17.0 | Helped |
| Use hierarchical RANSAC with minimal solvers instead of the compared methods | Low-rank matrix factorization · Dinosaur ~2000 pts | Frobenius error: 144.1 -> 48.45 | Helped |
| Use hierarchical RANSAC with minimal solvers instead of the compared methods | Low-rank matrix factorization · Linnaeus ~2000 pts | Frobenius error: 580000 -> 380.5 | Helped |
| Use hierarchical RANSAC with minimal solvers instead of the compared methods | Low-rank matrix factorization · Book | Frobenius error: 0.1740 -> 0.1740 | No clear effect |
| Use hierarchical RANSAC with minimal solvers instead of the compared methods | Low-rank matrix factorization · Hand | Frobenius error: 0.2802 -> 0.2802 | No clear effect |
| Add 10% missing data to the Book setting | Proposed · Book-10% | Frobenius error: 0.1740 -> 0.1534 | Helped |
| Add 10% missing data to the Hand setting | Proposed · Hand-10% | Frobenius error: 0.2802 -> 0.2634 | Helped |
| Add the GLUE step (ablation) | Proposed hierarchical RANSAC · Synthetic size test | truncated L2-error | No clear effect |
| Add the GLUE step (ablation) | Proposed hierarchical RANSAC · Synthetic size test | running time | Helped |
| Increase the input matrix size (ablation) | Proposed hierarchical RANSAC · Synthetic size test | running time | Mixed |
| Increase the outlier percentage (ablation) | Proposed hierarchical RANSAC · Synthetic outlier test | running time | Hurt |
| Use very small data-matrix bandwidth (ablation) | Proposed hierarchical RANSAC · Synthetic missing-data test | running time | Hurt |
Closest papers by meaning
- Compact Matrix Factorization With Dependent Subspaces (CVPR 2017 · similarity 0.89)
- High-Rank Matrix Completion and Clustering under Self-Expressive Models (NeurIPS 2016 · similarity 0.87)
- RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees (NeurIPS 2025 · similarity 0.86)
- Online High Rank Matrix Completion (CVPR 2019 · similarity 0.86)
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time (ICLR 2024 · similarity 0.84)
- Deep Rank-One Tensor Functional Factorization for Multi-Dimensional Data Recovery (AAAI 2025 · similarity 0.83)
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