Research map / Learning theory and algorithms / Matrix and tensor methods
Faster Algorithms for Binary Matrix Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the NMF-based baseline with k-means row clustering (k-means on rows, replace non-centers by closest center) | Binary matrix factorization (Frobenius error) · MNIST and ORL, k from 2 to 50 | average Frobenius error: e.g. MNIST k=10: 14.59 -> 2.52; ORL k=50: 76.58 -> 8.22 | Helped |
| Replace the NMF-based baseline with k-means row clustering | Binary matrix factorization (Frobenius error) · ORL, k=50 runtime | average time (ms): 412.92 -> 500.76 ms | Hurt |
| Use leverage-score sampling and enumeration (full theoretical algorithm) rather than the practical k-means-only version | Binary matrix factorization · MNIST/ORL | Frobenius error | No clear effect |
Closest papers by meaning
- Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization (ICML 2023 · similarity 0.95)
- Approximation Algorithms for $\ell_0$-Low Rank Approximation (NeurIPS 2017 · similarity 0.89)
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time (ICLR 2024 · similarity 0.86)
- Federated Binary Matrix Factorization Using Proximal Optimization (AAAI 2025 · similarity 0.85)
- The Structural Complexity of Matrix-Vector Multiplication (NeurIPS 2025 · similarity 0.85)
- Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation (ICLR 2025 · similarity 0.85)
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