Research map / Optimization / Nonconvex and smooth optimization
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PWMP instead of GCD | Least-squares non-negative matrix factorization · Reuters, K=10 | objective value: 5.9799e5 -> 5.9591e5 | Helped |
| Use FCMP instead of GCD | Least-squares non-negative matrix factorization · CBCL, K=10 | objective value: 2.2372e3 -> 2.2364e3 | Helped |
| Use PWMP instead of GCD | Least-squares non-negative matrix factorization · CBCL, K=10 | objective value: 2.2372e3 -> 2.2494e3 | Hurt |
| Use FCMP instead of GCD | Least-squares non-negative matrix factorization · CBCL, K=50 | objective value: 806 -> 786.15 | Helped |
| Use PWMP instead of GCD | Least-squares non-negative matrix factorization · CBCL, K=50 | objective value: 806 -> 789.901 | Helped |
| Use FCMP instead of GCD | Least-squares non-negative matrix factorization · KNIX, K=10 | objective value: 2.2372e03 -> 2.2364e03 | Helped |
| Use PWMP instead of GCD | Least-squares non-negative matrix factorization · KNIX, K=10 | objective value: 2.2372e03 -> 2.2494e03 | Hurt |
| Use NNMP instead of NNG | Non-negative garrote logistic regression · Sonar, training | accuracy: 0.8069 ± 0.0518 -> 0.8345 ± 0.0242 | Helped |
| Use PWMP instead of NNG | Non-negative garrote logistic regression · Sonar, training | accuracy: 0.8069 ± 0.0518 -> 0.8379 ± 0.0240 | Helped |
| Use FCMP instead of NNG | Non-negative garrote logistic regression · Sonar, training | accuracy: 0.8069 ± 0.0518 -> 0.8345 ± 0.0238 | Helped |
| Use NNMP instead of NNG | Non-negative garrote logistic regression · Sonar, test | accuracy: 0.7258 ± 0.0602 -> 0.7419 ± 0.0389 | Helped |
| Use PWMP instead of NNG | Non-negative garrote logistic regression · Sonar, test | accuracy: 0.7258 ± 0.0602 -> 0.7419 ± 0.0392 | Helped |
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