Research map / Learning theory and algorithms / Matrix and tensor methods
Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MEBF instead of MP | Boolean matrix factorization · Crime k=5 | coverage rate: 0.812 -> 0.835 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Crime k=20 | coverage rate: 0.807 -> 0.891 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Single cell k=5 | coverage rate: 0.463 -> 0.496 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Single cell k=20 | coverage rate: 0.580 -> 0.626 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Crime k=5 | density: 0.027 -> 0.019 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Crime k=20 | density: 0.066 -> 0.030 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Single cell k=5 | density: 2.86\text{e-4} -> 2.06\text{e-4} | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Single cell k=20 | density: 7.22\text{e-4} -> 3.34\text{e-4} | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Crime k=5 | time (s): 333.601 -> 2.913 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Crime k=20 | time (s): 992.011 -> 10.608 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Single cell k=5 | time (s): 137.623 -> 1.846 | Helped |
| Use MEBF instead of MP | Boolean matrix factorization · Single cell k=20 | time (s): 390.217 -> 5.954 | Helped |
Closest papers by meaning
- Federated Binary Matrix Factorization Using Proximal Optimization (AAAI 2025 · similarity 0.88)
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