Research map / Label-efficient and robust learning / Domain adaptation and generalization
Mutual Transfer Learning for Massive Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace K-means-based grouping with CD fusion using MCP penalty | learnability structure recovery · Simulation Cases 2, 5, 7 (Table 2) | NMI and perfect recovery rate | Helped |
| Use L1 (lasso) instead of concave fusion penalty (ablation) | CD fusion learnability recovery · Simulation Case 9 (n=2048, M=150, S=7) | NMI: 0.999 (MCP) vs 0.620 (L1) | Hurt |
| Use MCP instead of SCAD/TLP penalty (ablation) | CD fusion learnability recovery · Simulation Case 9 (n=2048, M=150, S=7) | Perfect recovery rate: 0.99 (MCP) vs 0.77 (SCAD), 0.81 (TLP) | Helped |
| CD fusion (MTL) vs full-data IPD estimation | computation time and peak memory · Simulation cases 1-9 | computation time and memory: MTL takes about 3/4 to 4/5 of IPD time and roughly half the memory | Helped |
| Use the MTL estimator on the NOAA nClimDiv data for prediction versus the unit-level baseline regression | test-set RMSE prediction · nClimDiv 2001-2016 test | test RMSE | Hurt |
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