Research map / Label-efficient and robust learning / Recognition and long-tail
Feature-Induced Labeling Information Enrichment for Multi-Label Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| enrich binary labels into numerical labels via sparse reconstruction in feature space (MLFE) vs recent feature-aware multi-label methods | MLFE · 15 multi-label benchmarks | average rank over six metrics | Helped |
| MLFE vs CLR and RAKEL | MLFE · 15 multi-label benchmarks | all six metrics | Helped |
| MLFE vs LIFT | MLFE · 15 multi-label benchmarks | one-error, ranking loss, average precision | No clear effect |
| MLFE vs RELIAB | MLFE · 15 multi-label benchmarks | macro/micro F1 | No clear effect |
| MLFE vs ML2 | MLFE · large-scale datasets | average precision: 0.316 vs 0.315 (corel5k); 0.615 vs 0.629 (rcv1subset1) | Mixed |
| MLFE enriched labels vs ML2 labels | MLFE · artificial data | KL divergence to ground truth: 0.1341 -> 0.1242 | Helped |
| MLFE vs k-means/fuzzy c-means/random numerical labels | MLFE · artificial data | average rank over six criteria: 1.333 vs 3.666 (KM), 3.000 (FCM), 4.666 (RANDOM) | Helped |
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