Research map / Learning theory and algorithms / Combinatorial and search optimization
Binary Matrix Factorisation via Column Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use column generation for Boolean k-BMF | BMF on eight real-world datasets · 24 dataset-rank instances, k=2,5,10 | factorisation error: 15 out of 24 | Helped |
| Use heuristic pricing rather than exact pricing | column generation on zoo · zoo, k=5,10 | runtime | Helped |
| Add multiple negative-reduced-cost columns at each iteration instead of adding a single heuristic column | column generation pricing · zoo, k=5,10 | runtime | Helped |
| Use MIP(1) rather than the compact KGH17 formulation | BMF on zoo and tumor · zoo and tumor | optimality gap: 0.0 vs 0.0; 0.9 vs 40.8 | Helped |
Closest papers by meaning
- Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent (NeurIPS 2022 · similarity 0.90)
- Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation (AAAI 2020 · similarity 0.88)
- Federated Binary Matrix Factorization Using Proximal Optimization (AAAI 2025 · similarity 0.87)
- Delegation-Relegation for Boolean Matrix Factorization (AAAI 2024 · similarity 0.86)
- Statistically Optimal $K$-means Clustering via Nonnegative Low-rank Semidefinite Programming (ICLR 2024 · similarity 0.83)
- The Structural Complexity of Matrix-Vector Multiplication (NeurIPS 2025 · similarity 0.82)
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