Research map / Learning theory and algorithms / Matrix and tensor methods
Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the FBCP completion approach with WDGTC | Hong Kong metro passenger flow · Hong Kong metro passenger flow test set | MSE (×10^6): 3.98 -> 2.68 | Helped |
| Replace the FBCP completion approach with WDGTC | Hong Kong metro passenger flow · Hong Kong metro passenger flow test set | MAPE (%): 172.81 -> 63 | Helped |
| Replace the MTIOP#LRS approach with WDGTC | Hong Kong metro passenger flow · Hong Kong metro passenger flow test set | MSE (×10^6): 6.15 -> 2.68 | Helped |
| Add weak-dependency penalty to low-rank completion (ablation) | WDGTC · Hong Kong metro passenger flow test set | MSE (×10^6): 8.32 -> 4.94 | Helped |
| Add POI graph penalty to weak-dependency and low-rank penalties (ablation) | WDGTC · Hong Kong metro passenger flow test set | MSE (×10^6): 4.94 -> 3.25 | Helped |
| Add NET graph penalty to weak-dependency, low-rank, and POI graph penalties (ablation) | WDGTC · Hong Kong metro passenger flow test set | MSE (×10^6): 3.25 -> 2.68 | Helped |
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