Research map / Label-efficient and robust learning / Recognition and long-tail
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Include the classifier-weight norm alongside MSE (ablation) | Analysis of feature transformations for 1NN · Text classification datasets | Correlation with 1NN error | Helped |
| Include the classifier-weight norm alongside MSE (ablation) | Analysis of feature transformations for 1NN · Computer vision datasets | Correlation with 1NN error | Helped |
| Use feature dimension alone to compare transformations (ablation) | 1NN · Text classification datasets | Correlation with 1NN error | Mixed |
| Use MSE alone, without the classifier-weight norm (ablation) | Analysis of feature transformations for 1NN · Text classification datasets | Correlation with 1NN error | Hurt |
| Increase the neighborhood size from k=1 to k=8 (ablation) | kNN correlation analysis using MSE and norm · SST2 | CCA score: 0.917 -> 0.97 | Helped |
Closest papers by meaning
- Rates of Convergence for Large-scale Nearest Neighbor Classification (NeurIPS 2019 · similarity 0.89)
- DNNR: Differential Nearest Neighbors Regression (ICML 2022 · similarity 0.89)
- Doubly Approximate Nearest Neighbor Classification (AAAI 2018 · similarity 0.88)
- An adaptive nearest neighbor rule for classification (NeurIPS 2019 · similarity 0.88)
- k*-Nearest Neighbors: From Global to Local (NeurIPS 2016 · similarity 0.87)
- Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later (ICLR 2025 · similarity 0.84)
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