Research map / Learning theory and algorithms / Kernels and regression theory
Rehashing Kernel Evaluation in High Dimensions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Hashing-based estimator (HBE) with sketching and adaptive sampling vs. baselines for kernel density evaluation | HBE · Real-world datasets (8 datasets, Table 1) | average query time: up to ~10x faster on some datasets, slower on others | Mixed |
| Random sampling vs HBE on a diagnosed-variance basis | Random sampling (RS) · Real-world datasets (Table 1) | average query time | Mixed |
| Diagnostic procedure to choose between RS and HBE | Variance-based diagnostic · Real-world datasets (Figure 3) | prediction accuracy of better estimator: correct for all but one dataset (SVHN) | Helped |
| HBE on worst-case synthetic instances with d in [10,500] | HBE · Synthetic worst-case instances | average query time | Helped |
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